Deep learning in neural networks: An overview

[1]  S. Achilefu IEEE International Symposium on Biomedical Imaging , 2018, IEEE Pulse.

[2]  Hema R. Madala,et al.  Inductive Learning Algorithms for Complex Systems Modeling , 2017 .

[3]  Terrence J. Sejnowski,et al.  Unsupervised Learning , 2018, Encyclopedia of GIS.

[4]  C. Lee Giles,et al.  The Neural Network Pushdown Automaton: Model, Stack and Learning Simulations , 2017, ArXiv.

[5]  R. Miikkulainen Evolving Neural Networks , 2016, GECCO.

[6]  Hillol Kargupta,et al.  Graphical Models: Foundations of Neural Computation , 2016, Pattern Analysis and Applications.

[7]  Tapani Raiko,et al.  International Conference on Learning Representations (ICLR) , 2016 .

[8]  Doreen Meier,et al.  Neural Network Design And The Complexity Of Learning , 2016 .

[9]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[10]  Dan Ciresan,et al.  Multi-Column Deep Neural Networks for offline handwritten Chinese character classification , 2013, 2015 International Joint Conference on Neural Networks (IJCNN).

[11]  Andreas Krause,et al.  Advances in Neural Information Processing Systems (NIPS) , 2014 .

[12]  Honglak Lee,et al.  Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning , 2014, NIPS.

[13]  Dinggang Shen,et al.  Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis , 2014, MICCAI.

[14]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[15]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[16]  Ling Shao,et al.  Leveraging Hierarchical Parametric Networks for Skeletal Joints Based Action Segmentation and Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Mohammed Bennamoun,et al.  Automatic Feature Learning for Robust Shadow Detection , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Fei-Fei Li,et al.  Large-Scale Video Classification with Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[19]  Ivan Laptev,et al.  Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Navdeep Jaitly,et al.  Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.

[21]  Dong Yu,et al.  Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..

[22]  Christian Osendorfer,et al.  Variational inference of latent state sequences using Recurrent Networks , 2014, ArXiv.

[23]  Erik Marchi,et al.  Multi-resolution linear prediction based features for audio onset detection with bidirectional LSTM neural networks , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[24]  Björn W. Schuller,et al.  Social signal classification using deep blstm recurrent neural networks , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[25]  Pierre Baldi,et al.  The dropout learning algorithm , 2014, Artif. Intell..

[26]  Christopher Kermorvant,et al.  The A2iA Arabic Handwritten Text Recognition System at the Open HaRT2013 Evaluation , 2014, 2014 11th IAPR International Workshop on Document Analysis Systems.

[27]  Wulfram Gerstner,et al.  Stochastic variational learning in recurrent spiking networks , 2014, Front. Comput. Neurosci..

[28]  Volkmar Frinken,et al.  Neural network language models for off-line handwriting recognition , 2014, Pattern Recognition.

[29]  Nikola K. Kasabov,et al.  NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data , 2014, Neural Networks.

[30]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[31]  Ling Shao,et al.  Learning Deep and Wide: A Spectral Method for Learning Deep Networks , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[32]  Wolfgang Maass,et al.  Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning. , 2014, Cerebral cortex.

[33]  Jürgen Schmidhuber,et al.  A Clockwork RNN , 2014, ICML.

[34]  Steven R. Young,et al.  Hierarchical spatiotemporal feature extraction using recurrent online clustering , 2014, Pattern Recognit. Lett..

[35]  Shih-Chii Liu,et al.  Minitaur, an Event-Driven FPGA-Based Spiking Network Accelerator , 2014, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[36]  Carolo Friederico Gauss Theoria Motus Corporum Coelestium in Sectionibus Conicis Solem Ambientium , 2014 .

[37]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[38]  Yoshua Bengio,et al.  An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks , 2013, ICLR.

[39]  Garrison W. Cottrell,et al.  Efficient Visual Coding: From Retina To V2 , 2013, ICLR.

[40]  Razvan Pascanu,et al.  How to Construct Deep Recurrent Neural Networks , 2013, ICLR.

[41]  Yaroslav Bulatov,et al.  Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks , 2013, ICLR.

[42]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[43]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[44]  Christopher Kermorvant,et al.  Dropout Improves Recurrent Neural Networks for Handwriting Recognition , 2013, 2014 14th International Conference on Frontiers in Handwriting Recognition.

[45]  Gert Cauwenberghs,et al.  Event-driven contrastive divergence for spiking neuromorphic systems , 2013, Front. Neurosci..

[46]  Christian Osendorfer,et al.  On Fast Dropout and its Applicability to Recurrent Networks , 2013, ICLR.

[47]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[48]  Mathias Johansson Blind Source Separation , 2014, Encyclopedia of Social Network Analysis and Mining.

[49]  Kyunghyun Cho,et al.  Foundations and Advances in Deep Learning , 2014 .

[50]  Bhuvana Ramabhadran,et al.  Prosody contour prediction with long short-term memory, bi-directional, deep recurrent neural networks , 2014, INTERSPEECH.

[51]  Georg Heigold,et al.  Sequence discriminative distributed training of long short-term memory recurrent neural networks , 2014, INTERSPEECH.

[52]  Björn W. Schuller,et al.  Robust speech recognition using long short-term memory recurrent neural networks for hybrid acoustic modelling , 2014, INTERSPEECH.

[53]  Frank K. Soong,et al.  TTS synthesis with bidirectional LSTM based recurrent neural networks , 2014, INTERSPEECH.

[54]  Joaquín González-Rodríguez,et al.  Automatic language identification using long short-term memory recurrent neural networks , 2014, INTERSPEECH.

[55]  Christian Igel,et al.  Training restricted Boltzmann machines: An introduction , 2014, Pattern Recognit..

[56]  Andrew W. Senior,et al.  Long short-term memory recurrent neural network architectures for large scale acoustic modeling , 2014, INTERSPEECH.

[57]  Jürgen Schmidhuber,et al.  My First Deep Learning System of 1991 + Deep Learning Timeline 1962-2013 , 2013, ArXiv.

[58]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[59]  Brendan J. Frey,et al.  Adaptive dropout for training deep neural networks , 2013, NIPS.

[60]  Ha Hong,et al.  Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream , 2013, NIPS.

[61]  Dumitru Erhan,et al.  Deep Neural Networks for Object Detection , 2013, NIPS.

[62]  Jürgen Schmidhuber,et al.  Compete to Compute , 2013, NIPS.

[63]  Eduardo F. Morales,et al.  Qualitative Transfer for Reinforcement Learning with Continuous State and Action Spaces , 2013, CIARP.

[64]  Tobi Delbruck,et al.  Real-time classification and sensor fusion with a spiking deep belief network , 2013, Front. Neurosci..

[65]  Luca Maria Gambardella,et al.  Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks , 2013, MICCAI.

[66]  Fei Yin,et al.  ICDAR 2013 Chinese Handwriting Recognition Competition , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[67]  Thomas M. Breuel,et al.  High-Performance OCR for Printed English and Fraktur Using LSTM Networks , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[68]  Sinan Kalkan,et al.  Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision? , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[69]  Camille Couprie,et al.  Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[70]  Wolfgang Maass,et al.  Emergence of Dynamic Memory Traces in Cortical Microcircuit Models through STDP , 2013, The Journal of Neuroscience.

[71]  Julian Francis Miller,et al.  Cartesian genetic programming encoded artificial neural networks: a comparison using three benchmarks , 2013, GECCO '13.

[72]  Jürgen Schmidhuber,et al.  Evolving large-scale neural networks for vision-based reinforcement learning , 2013, GECCO '13.

[73]  Shimon Whiteson,et al.  Critical factors in the performance of hyperNEAT , 2013, GECCO '13.

[74]  Pierre Baldi,et al.  Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules , 2013, J. Chem. Inf. Model..

[75]  Christopher D. Manning,et al.  Fast dropout training , 2013, ICML.

[76]  Tao Wang,et al.  Deep learning with COTS HPC systems , 2013, ICML.

[77]  W. Senn,et al.  Matching Recall and Storage in Sequence Learning with Spiking Neural Networks , 2013, The Journal of Neuroscience.

[78]  Jürgen Schmidhuber,et al.  An intrinsic value system for developing multiple invariant representations with incremental slowness learning , 2013, Front. Neurorobot..

[79]  Tara N. Sainath,et al.  Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[80]  Björn W. Schuller,et al.  Real-life voice activity detection with LSTM Recurrent Neural Networks and an application to Hollywood movies , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[81]  Chris Eliasmith,et al.  How to Build a Brain: A Neural Architecture for Biological Cognition , 2013 .

[82]  Daniele Loiacono,et al.  Simulated Car Racing Championship: Competition Software Manual , 2013, ArXiv.

[83]  Kunihiko Fukushima,et al.  Training multi-layered neural network neocognitron , 2013, Neural Networks.

[84]  Wolfgang Maass,et al.  Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity , 2013, PLoS Comput. Biol..

[85]  Marco Mirolli,et al.  Intrinsically Motivated Learning in Natural and Artificial Systems , 2013 .

[86]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[87]  Tapani Raiko,et al.  Enhanced Gradient for Training Restricted Boltzmann Machines , 2013, Neural Computation.

[88]  Yoshua Bengio,et al.  Maxout Networks , 2013, ICML.

[89]  Luca Maria Gambardella,et al.  Fast image scanning with deep max-pooling convolutional neural networks , 2013, 2013 IEEE International Conference on Image Processing.

[90]  Jürgen Schmidhuber,et al.  A fast learning algorithm for image segmentation with max-pooling convolutional networks , 2013, 2013 IEEE International Conference on Image Processing.

[91]  Björn W. Schuller,et al.  Keyword spotting exploiting Long Short-Term Memory , 2013, Speech Commun..

[92]  Pierre-Yves Oudeyer,et al.  Intrinsically Motivated Learning of Real-World Sensorimotor Skills with Developmental Constraints , 2013, Intrinsically Motivated Learning in Natural and Artificial Systems.

[93]  Sergio Davies,et al.  Learning in spiking neural networks , 2013 .

[94]  Maneesh Sahani,et al.  Regularization and nonlinearities for neural language models: when are they needed? , 2013, ArXiv.

[95]  Maria S. Kulikova,et al.  Mitosis detection in breast cancer histological images An ICPR 2012 contest , 2013, Journal of pathology informatics.

[96]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[97]  Hod Lipson,et al.  The evolutionary origins of modularity , 2012, Proceedings of the Royal Society B: Biological Sciences.

[98]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[99]  Tom Schaul,et al.  No more pesky learning rates , 2012, ICML.

[100]  Marc'Aurelio Ranzato,et al.  Building high-level features using large scale unsupervised learning , 2011, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[101]  Jürgen Schmidhuber,et al.  PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem , 2011, Front. Psychol..

[102]  David J. Jilk,et al.  Recurrent Processing during Object Recognition , 2011, Front. Psychol..

[103]  Tom Schaul,et al.  A linear time natural evolution strategy for non-separable functions , 2011, GECCO.

[104]  Ming Yang,et al.  3D Convolutional Neural Networks for Human Action Recognition , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[105]  Kunihiko Fukushima,et al.  Artificial vision by multi-layered neural networks: Neocognitron and its advances , 2013, Neural Networks.

[106]  Andrew L. Maas Rectifier Nonlinearities Improve Neural Network Acoustic Models , 2013 .

[107]  S. Hochreiter,et al.  Sequence Classification For Protein Analysis , 2013 .

[108]  Fei Yin,et al.  Chinese Handwriting Recognition Competition , 2013 .

[109]  Laurenz Wiskott,et al.  How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis , 2013, J. Mach. Learn. Res..

[110]  Marco Wiering,et al.  Reinforcement Learning , 2014, Adaptation, Learning, and Optimization.

[111]  Matthew D. Zeiler ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[112]  Biao Huang,et al.  System Identification , 2000, Control Theory for Physicists.

[113]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[114]  Luca Maria Gambardella,et al.  Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images , 2012, NIPS.

[115]  Yan Meng,et al.  > Replace This Line with Your Paper Identification Number (double-click Here to Edit) < 1 , 2022 .

[116]  Trevor Bekolay,et al.  A Large-Scale Model of the Functioning Brain , 2012, Science.

[117]  Jürgen Schmidhuber,et al.  Incremental Slow Feature Analysis: Adaptive Low-Complexity Slow Feature Updating from High-Dimensional Input Streams , 2012, Neural Computation.

[118]  Robert Babuska,et al.  A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[119]  Volkmar Frinken,et al.  Long-short term memory neural networks language modeling for handwriting recognition , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[120]  Tim Curran,et al.  The Limits of Feedforward Vision: Recurrent Processing Promotes Robust Object Recognition when Objects Are Degraded , 2012, Journal of Cognitive Neuroscience.

[121]  Tara N. Sainath,et al.  Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups , 2012, IEEE Signal Processing Magazine.

[122]  E. D’Angelo The human brain project. , 2012, Functional neurology.

[123]  Pierre Baldi,et al.  Deep architectures for protein contact map prediction , 2012, Bioinform..

[124]  Jürgen Schmidhuber,et al.  Self-Delimiting Neural Networks , 2012, ArXiv.

[125]  Sebastian Otte,et al.  Local Feature Based Online Mode Detection with Recurrent Neural Networks , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.

[126]  Volkmar Frinken,et al.  Mode Detection in Online Handwritten Documents Using BLSTM Neural Networks , 2012, 2012 International Conference on Frontiers in Handwriting Recognition.

[127]  Punit Shah Toward a Neurobiology of Unrealistic Optimism , 2012, Front. Psychology.

[128]  Tapani Raiko,et al.  Tikhonov-Type Regularization for Restricted Boltzmann Machines , 2012, ICANN.

[129]  Johannes Stallkamp,et al.  Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition , 2012, Neural Networks.

[130]  Jürgen Schmidhuber,et al.  Multi-column deep neural network for traffic sign classification , 2012, Neural Networks.

[131]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[132]  Yoshua Bengio,et al.  Large-Scale Feature Learning With Spike-and-Slab Sparse Coding , 2012, ICML.

[133]  Martin A. Riedmiller,et al.  Autonomous reinforcement learning on raw visual input data in a real world application , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[134]  Sebastian Risi,et al.  A unified approach to evolving plasticity and neural geometry , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[135]  Jürgen Schmidhuber,et al.  Transfer learning for Latin and Chinese characters with Deep Neural Networks , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[136]  Fred Henrik Hamker,et al.  Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex , 2012, Neural Computation.

[137]  Tapani Raiko,et al.  Deep Learning Made Easier by Linear Transformations in Perceptrons , 2012, AISTATS.

[138]  Jürgen Schmidhuber,et al.  Multi-column deep neural networks for image classification , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[139]  James J. DiCarlo,et al.  How Does the Brain Solve Visual Object Recognition? , 2012, Neuron.

[140]  Marco Zorzi,et al.  Emergence of a 'visual number sense' in hierarchical generative models , 2012, Nature Neuroscience.

[141]  Yoshua Bengio,et al.  Spike-and-Slab Sparse Coding for Unsupervised Feature Discovery , 2012, ArXiv.

[142]  Dong Yu,et al.  Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition , 2012, IEEE Transactions on Audio, Speech, and Language Processing.

[143]  David Barber,et al.  On the Computational Complexity of Stochastic Controller Optimization in POMDPs , 2011, TOCT.

[144]  Pierre Baldi,et al.  Autoencoders, Unsupervised Learning, and Deep Architectures , 2011, ICML Unsupervised and Transfer Learning.

[145]  Yoshua Bengio,et al.  Unsupervised and Transfer Learning Challenge: a Deep Learning Approach , 2011, ICML Unsupervised and Transfer Learning.

[146]  Shimon Whiteson,et al.  Evolutionary Computation for Reinforcement Learning , 2012, Reinforcement Learning.

[147]  Pravin Karde,et al.  Traffic Sign Recognition , 2012 .

[148]  R. Kurzweil How to Create a Mind: The Secret of Human Thought Revealed , 2012 .

[149]  Hans-Georg Zimmermann,et al.  Forecasting with Recurrent Neural Networks: 12 Tricks , 2012, Neural Networks: Tricks of the Trade.

[150]  Yee Whye Teh,et al.  Actor-Critic Reinforcement Learning with Energy-Based Policies , 2012, EWRL.

[151]  Alex Graves,et al.  Practical Variational Inference for Neural Networks , 2011, NIPS.

[152]  Ahmad Salman,et al.  Learning Speaker-Specific Characteristics With a Deep Neural Architecture , 2011, IEEE Transactions on Neural Networks.

[153]  Tom Schaul,et al.  The two-dimensional organization of behavior , 2011, 2011 IEEE International Conference on Development and Learning (ICDL).

[154]  Jürgen Schmidhuber,et al.  A committee of neural networks for traffic sign classification , 2011, The 2011 International Joint Conference on Neural Networks.

[155]  Johannes Stallkamp,et al.  The German Traffic Sign Recognition Benchmark: A multi-class classification competition , 2011, The 2011 International Joint Conference on Neural Networks.

[156]  Yann LeCun,et al.  Traffic sign recognition with multi-scale Convolutional Networks , 2011, The 2011 International Joint Conference on Neural Networks.

[157]  Volkmar Frinken,et al.  Keyword Spotting in Online Handwritten Documents Containing Text and Non-text Using BLSTM Neural Networks , 2011, 2011 International Conference on Document Analysis and Recognition.

[158]  Kunihiko Fukushima,et al.  Increasing robustness against background noise: Visual pattern recognition by a neocognitron , 2011, Neural Networks.

[159]  Jürgen Schmidhuber,et al.  Sequential Constant Size Compressors for Reinforcement Learning , 2011, AGI.

[160]  Jürgen Schmidhuber,et al.  On Fast Deep Nets for AGI Vision , 2011, AGI.

[161]  Jurgen Schmidhuber,et al.  Intrinsically motivated neuroevolution for vision-based reinforcement learning , 2011, 2011 IEEE International Conference on Development and Learning (ICDL).

[162]  Luca Maria Gambardella,et al.  Flexible, High Performance Convolutional Neural Networks for Image Classification , 2011, IJCAI.

[163]  Pedro M. Domingos,et al.  Sum-product networks: A new deep architecture , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[164]  Ilya Sutskever,et al.  Learning Recurrent Neural Networks with Hessian-Free Optimization , 2011, ICML.

[165]  Pascal Vincent,et al.  Contractive Auto-Encoders: Explicit Invariance During Feature Extraction , 2011, ICML.

[166]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[167]  Kenneth O. Stanley,et al.  On the Performance of Indirect Encoding Across the Continuum of Regularity , 2011, IEEE Transactions on Evolutionary Computation.

[168]  Björn W. Schuller,et al.  Online Driver Distraction Detection Using Long Short-Term Memory , 2011, IEEE Transactions on Intelligent Transportation Systems.

[169]  Christoph Bregler,et al.  Learning invariance through imitation , 2011, CVPR 2011.

[170]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

[171]  Niko Wilbert,et al.  Slow feature analysis , 2011, Scholarpedia.

[172]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[173]  M. Knoche Kat. 16 Das Gründungsdokument der modernen Naturwissenschaft Isaac Newton: Philosophiae naturalis principia mathematica. Londini: jussi Societatus Regiae ac typis Josephi Streater / London: John Streater und Samuel Smith, 1687 , 2011 .

[174]  Mohamed,et al.  The Group Method of Data Handling (GMDH) and Artificial Neural Networks (ANN)in Time-Series Forecasting of Rice Yield , 2011 .

[175]  A. Sayed,et al.  Foundations and Trends ® in Machine Learning > Vol 7 > Issue 4-5 Ordering Info About Us Alerts Contact Help Log in Adaptation , Learning , and Optimization over Networks , 2011 .

[176]  Faustino J. Gomez,et al.  Intrinsically Motivated Evolutionary Search for Vision-Based Reinforcement Learning , 2011 .

[177]  Björn Schuller,et al.  On-line Driver Distraction Detection using Long Short-Term Memory , 2011 .

[178]  Jan Peters,et al.  Policy Gradient Methods , 2010, Encyclopedia of Machine Learning.

[179]  Kenji Doya,et al.  Free-Energy Based Reinforcement Learning for Vision-Based Navigation with High-Dimensional Sensory Inputs , 2010, ICONIP.

[180]  Derek C. Rose,et al.  Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier] , 2010, IEEE Computational Intelligence Magazine.

[181]  Jürgen Schmidhuber,et al.  Recurrent policy gradients , 2010, Log. J. IGPL.

[182]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[183]  Sven Behnke,et al.  Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.

[184]  Tom Schaul,et al.  Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients , 2010, ICANN.

[185]  Steve B. Furber,et al.  Modeling Spiking Neural Networks on SpiNNaker , 2010, Computing in Science & Engineering.

[186]  Simei Gomes Wysoski,et al.  Evolving spiking neural networks for audiovisual information processing , 2010, Neural Networks.

[187]  Robert A. Legenstein,et al.  Reinforcement Learning on Slow Features of High-Dimensional Input Streams , 2010, PLoS Comput. Biol..

[188]  Gul Muhammad Khan,et al.  Evolution of neural networks using Cartesian Genetic Programming , 2010, IEEE Congress on Evolutionary Computation.

[189]  Tom Schaul,et al.  Exponential natural evolution strategies , 2010, GECCO '10.

[190]  Jürgen Schmidhuber,et al.  Evolving neural networks in compressed weight space , 2010, GECCO '10.

[191]  Martin A. Riedmiller,et al.  Deep auto-encoder neural networks in reinforcement learning , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[192]  Richard S. Sutton,et al.  GQ(lambda): A general gradient algorithm for temporal-difference prediction learning with eligibility traces , 2010, Artificial General Intelligence.

[193]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[194]  James Martens,et al.  Deep learning via Hessian-free optimization , 2010, ICML.

[195]  Geoffrey E. Hinton,et al.  Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines , 2010, Neural Computation.

[196]  Julian Togelius,et al.  The 2009 Simulated Car Racing Championship , 2010, IEEE Transactions on Computational Intelligence and AI in Games.

[197]  Danil V. Prokhorov,et al.  A Convolutional Learning System for Object Classification in 3-D Lidar Data , 2010, IEEE Transactions on Neural Networks.

[198]  Frank Sehnke,et al.  Parameter-exploring policy gradients , 2010, Neural Networks.

[199]  Makoto Otsuka Goal-Oriented Representations of the External World : A Free-Energy-Based Approach , 2010 .

[200]  Geoffrey E. Hinton,et al.  Phone recognition using Restricted Boltzmann Machines , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[201]  Yoshua Bengio,et al.  Why Does Unsupervised Pre-training Help Deep Learning? , 2010, AISTATS.

[202]  Luca Maria Gambardella,et al.  Deep, Big, Simple Neural Nets for Handwritten Digit Recognition , 2010, Neural Computation.

[203]  Joseph F. Murray,et al.  Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation , 2010, Neural Computation.

[204]  Julian Francis Miller,et al.  Cartesian genetic programming , 2000, GECCO '10.

[205]  大塚 誠,et al.  Goal-oriented representations of the external world : a free-energy-based approach , 2010 .

[206]  Subhash C. Kak,et al.  Data Mining Using Surface and Deep Agents Based on Neural Networks , 2010, AMCIS.

[207]  R. Sutton,et al.  GQ(λ): A general gradient algorithm for temporal-difference prediction learning with eligibility traces , 2010 .

[208]  杉本 剛 Philosophiae Naturalis Principia Mathematica邦訳書の底本に関するノート , 2010 .

[209]  Luca Maria Gambardella,et al.  Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition , 2010, ArXiv.

[210]  Lise Getoor,et al.  Learning in Logic , 2010, Encyclopedia of Machine Learning.

[211]  Junichiro Yoshimoto,et al.  Free-energy-based reinforcement learning in a partially observable environment , 2010, ESANN.

[212]  Peter Stone,et al.  Reinforcement learning , 2019, Scholarpedia.

[213]  Prashant Parikh A Theory of Communication , 2010 .

[214]  Honglak Lee,et al.  Unsupervised feature learning for audio classification using convolutional deep belief networks , 2009, NIPS.

[215]  Julian Togelius,et al.  Evolving Memory Cell Structures for Sequence Learning , 2009, ICANN.

[216]  Verena Heidrich-Meisner,et al.  Neuroevolution strategies for episodic reinforcement learning , 2009, J. Algorithms.

[217]  Tobi Delbrück,et al.  CAVIAR: A 45k Neuron, 5M Synapse, 12G Connects/s AER Hardware Sensory–Processing– Learning–Actuating System for High-Speed Visual Object Recognition and Tracking , 2009, IEEE Transactions on Neural Networks.

[218]  Tom Schaul,et al.  Efficient natural evolution strategies , 2009, GECCO.

[219]  Geoffrey E. Hinton,et al.  Semantic hashing , 2009, Int. J. Approx. Reason..

[220]  Rajat Raina,et al.  Large-scale deep unsupervised learning using graphics processors , 2009, ICML '09.

[221]  Honglak Lee,et al.  Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations , 2009, ICML '09.

[222]  Jianlin Cheng,et al.  NNcon: improved protein contact map prediction using 2D-recursive neural networks , 2009, Nucleic Acids Res..

[223]  J. Schmidhuber,et al.  A Novel Connectionist System for Unconstrained Handwriting Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[224]  Kenneth O. Stanley,et al.  A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks , 2009, Artificial Life.

[225]  Victor Uc Cetina,et al.  Reinforcement learning in continuous state and action spaces , 2009 .

[226]  Daniel Rueckert,et al.  Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II , 2017, Lecture Notes in Computer Science.

[227]  Dimitri P. Bertsekas,et al.  Neuro-Dynamic Programming , 2009, Encyclopedia of Optimization.

[228]  Ming Yang,et al.  Detecting Human Actions in Surveillance Videos , 2009, TRECVID.

[229]  Geoffrey E. Hinton,et al.  Deep Belief Networks for phone recognition , 2009 .

[230]  Seungjin Choi,et al.  Independent Component Analysis , 2009, Handbook of Natural Computing.

[231]  A. Antunes Democracia e Cidadania na Escola: Do Discurso à Prática , 2008 .

[232]  Keiji Tanaka,et al.  Matching Categorical Object Representations in Inferior Temporal Cortex of Man and Monkey , 2008, Neuron.

[233]  R. Sutton,et al.  A convergent O ( n ) algorithm for off-policy temporal-difference learning with linear function approximation , 2008, NIPS 2008.

[234]  M. Graziano The Intelligent Movement Machine: An Ethological Perspective on the Primate Motor System , 2008 .

[235]  Geoffrey E. Hinton,et al.  The Recurrent Temporal Restricted Boltzmann Machine , 2008, NIPS.

[236]  Andrew G. Barto,et al.  Skill Characterization Based on Betweenness , 2008, NIPS.

[237]  H. Sebastian Seung,et al.  Natural Image Denoising with Convolutional Networks , 2008, NIPS.

[238]  Lin Wu,et al.  Learning to play Go using recursive neural networks , 2008, Neural Networks.

[239]  Jürgen Schmidhuber,et al.  State-Dependent Exploration for Policy Gradient Methods , 2008, ECML/PKDD.

[240]  Steffen Udluft,et al.  Learning long-term dependencies with recurrent neural networks , 2008, Neurocomputing.

[241]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[242]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[243]  Tom Schaul,et al.  Natural Evolution Strategies , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[244]  Luis A. Plana,et al.  SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[245]  Risto Miikkulainen,et al.  Accelerated Neural Evolution through Cooperatively Coevolved Synapses , 2008, J. Mach. Learn. Res..

[246]  Johannes Schemmel,et al.  Realizing biological spiking network models in a configurable wafer-scale hardware system , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[247]  Christof Koch,et al.  Unsupervised Learning of Individuals and Categories from Images , 2008, Neural Computation.

[248]  Stefan Schaal,et al.  2008 Special Issue: Reinforcement learning of motor skills with policy gradients , 2008 .

[249]  Stefan Schaal,et al.  Natural Actor-Critic , 2003, Neurocomputing.

[250]  T. Munich,et al.  Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks , 2008, NIPS.

[251]  Tadashi Kondo,et al.  Multi-layered GMDH-type neural network self-selecting optimum neural network architecture and its application to 3-dimensional medical image recognition of blood vessels , 2008 .

[252]  Felix L. Chernousko,et al.  Control of Nonlinear Dynamical Systems , 2008 .

[253]  Richard S. Sutton,et al.  A Convergent O(n) Temporal-difference Algorithm for Off-policy Learning with Linear Function Approximation , 2008, NIPS.

[254]  Michel Verleysen,et al.  Nonlinear Dimensionality Reduction , 2021, Computer Vision.

[255]  Jürgen Schmidhuber,et al.  Unconstrained On-line Handwriting Recognition with Recurrent Neural Networks , 2007, NIPS.

[256]  Honglak Lee,et al.  Sparse deep belief net model for visual area V2 , 2007, NIPS.

[257]  Jürgen Schmidhuber,et al.  An Application of Recurrent Neural Networks to Discriminative Keyword Spotting , 2007, ICANN.

[258]  Jürgen Schmidhuber,et al.  Solving Deep Memory POMDPs with Recurrent Policy Gradients , 2007, ICANN.

[259]  Radford M. Neal Pattern Recognition and Machine Learning , 2007, Technometrics.

[260]  Laurenz Wiskott,et al.  Slowness and Sparseness Lead to Place, Head-Direction, and Spatial-View Cells , 2007, PLoS Comput. Biol..

[261]  Kenneth O. Stanley,et al.  A novel generative encoding for exploiting neural network sensor and output geometry , 2007, GECCO '07.

[262]  Marc'Aurelio Ranzato,et al.  Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[263]  Jürgen Schmidhuber,et al.  Prototype Resilient, Self-Modeling Robots , 2007, Science.

[264]  Jochen J. Steil,et al.  Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning , 2007, Neural Networks.

[265]  Anitha Pasupathy,et al.  Transformation of shape information in the ventral pathway , 2007, Current Opinion in Neurobiology.

[266]  Jürgen Schmidhuber,et al.  Training Recurrent Networks by Evolino , 2007, Neural Computation.

[267]  Jürgen Schmidhuber,et al.  Sequence Labelling in Structured Domains with Hierarchical Recurrent Neural Networks , 2007, IJCAI.

[268]  Nicholas T. Carnevale,et al.  Simulation of networks of spiking neurons: A review of tools and strategies , 2006, Journal of Computational Neuroscience.

[269]  Gene H. Golub,et al.  Generalized cross-validation as a method for choosing a good ridge parameter , 1979, Milestones in Matrix Computation.

[270]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[271]  Jürgen Schmidhuber,et al.  Gödel Machines: Fully Self-referential Optimal Universal Self-improvers , 2007, Artificial General Intelligence.

[272]  Susan A. Murphy,et al.  Monographs on statistics and applied probability , 1990 .

[273]  Benjamin Schrauwen,et al.  An overview of reservoir computing: theory, applications and implementations , 2007, ESANN.

[274]  A. Graves,et al.  Unconstrained Online Handwriting Recognition with Recurrent Neural Networks , 2007 .

[275]  Jürgen Schmidhuber,et al.  RNN-based Learning of Compact Maps for Efficient Robot Localization , 2007, ESANN.

[276]  Patrice Simardy,et al.  Learning Long-Term Dependencies with , 2007 .

[277]  Justus H. Piater,et al.  Closed-Loop Learning of Visual Control Policies , 2011, J. Artif. Intell. Res..

[278]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[279]  Marc'Aurelio Ranzato,et al.  Efficient Learning of Sparse Representations with an Energy-Based Model , 2006, NIPS.

[280]  Garrison W. Cottrell,et al.  Recursive ICA , 2006, NIPS.

[281]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[282]  Shimon Whiteson,et al.  Evolutionary Function Approximation for Reinforcement Learning , 2006, J. Mach. Learn. Res..

[283]  Johannes Schemmel,et al.  Implementing Synaptic Plasticity in a VLSI Spiking Neural Network Model , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[284]  Patrice Y. Simard,et al.  High Performance Convolutional Neural Networks for Document Processing , 2006 .

[285]  Jürgen Schmidhuber,et al.  A System for Robotic Heart Surgery that Learns to Tie Knots Using Recurrent Neural Networks , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[286]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[287]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[288]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[289]  Jürgen Schmidhuber,et al.  Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks , 2006, ICML.

[290]  Yann LeCun,et al.  Dimensionality Reduction by Learning an Invariant Mapping , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[291]  Józef Korbicz,et al.  A GMDH neural network-based approach to robust fault diagnosis : Application to the DAMADICS benchmark problem , 2006 .

[292]  Jürgen Schmidhuber,et al.  Optimal Artificial Curiosity, Creativity, Music, and the Fine Arts , 2005 .

[293]  Frédéric Jurie,et al.  Sampling Strategies for Bag-of-Features Image Classification , 2006, ECCV.

[294]  Barnabás Póczos,et al.  Cross-Entropy Optimization for Independent Process Analysis , 2006, ICA.

[295]  Michael S. Falconbridge,et al.  A Simple Hebbian/Anti-Hebbian Network Learns the Sparse, Independent Components of Natural Images , 2006, Neural Computation.

[296]  Michael J. Frank,et al.  Making Working Memory Work: A Computational Model of Learning in the Prefrontal Cortex and Basal Ganglia , 2006, Neural Computation.

[297]  J Quinonero Candela,et al.  Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment , 2006, Lecture Notes in Computer Science.

[298]  Radford M. Neal,et al.  High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees , 2006, Feature Extraction.

[299]  P. Werbos Backwards Differentiation in AD and Neural Nets: Past Links and New Opportunities , 2006 .

[300]  D. George,et al.  Hierarchical Temporal Memory Concepts , Theory , and Terminology , 2006 .

[301]  Yann LeCun,et al.  Off-Road Obstacle Avoidance through End-to-End Learning , 2005, NIPS.

[302]  Tomaso Poggio,et al.  Fast Readout of Object Identity from Macaque Inferior Temporal Cortex , 2005, Science.

[303]  Martin A. Riedmiller Neural Fitted Q Iteration - First Experiences with a Data Efficient Neural Reinforcement Learning Method , 2005, ECML.

[304]  Peter Lennie,et al.  Coding of color and form in the geniculostriate visual pathway (invited review). , 2005, Journal of the Optical Society of America. A, Optics, image science, and vision.

[305]  Jürgen Schmidhuber,et al.  Classifying Unprompted Speech by Retraining LSTM Nets , 2005, ICANN.

[306]  Jürgen Schmidhuber,et al.  Co-evolving recurrent neurons learn deep memory POMDPs , 2005, GECCO '05.

[307]  Qingxiang Wu,et al.  A Novel Approach for the Implementation of Large Scale Spiking Neural Networks on FPGA Hardware , 2005, IWANN.

[308]  Jürgen Schmidhuber,et al.  Framewise phoneme classification with bidirectional LSTM and other neural network architectures , 2005, Neural Networks.

[309]  Risto Miikkulainen,et al.  Evolving Soccer Keepaway Players Through Task Decomposition , 2005, Machine Learning.

[310]  Robert Desimone,et al.  Parallel and Serial Neural Mechanisms for Visual Search in Macaque Area V4 , 2005, Science.

[311]  Radford M. Neal Classification with Bayesian Neural Networks , 2005, MLCW.

[312]  Mark A. Pitt,et al.  Advances in Minimum Description Length: Theory and Applications , 2005 .

[313]  E. Rolls,et al.  Neurodynamics of biased competition and cooperation for attention: a model with spiking neurons. , 2005, Journal of neurophysiology.

[314]  David Windisch Loading Deep Networks Is Hard: The Pyramidal Case , 2005, Neural Computation.

[315]  Jun Morimoto,et al.  Robust Reinforcement Learning , 2005, Neural Computation.

[316]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[317]  Alexander Gloye,et al.  Reinforcing the Driving Quality of Soccer Playing Robots by Anticipation (Verbesserung der Fahreigenschaften von fußballspielenden Robotern durch Antizipation) , 2005, it Inf. Technol..

[318]  SRIDHAR MAHADEVAN,et al.  Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results , 2005, Machine Learning.

[319]  Bram Bakker,et al.  Hierarchical Reinforcement Learning Based on Subgoal Discovery and Subpolicy Specialization , 2003 .

[320]  Marcus Hutter Simulation Algorithms for Computational Systems Biology , 2017, Texts in Theoretical Computer Science. An EATCS Series.

[321]  Sven Behnke,et al.  Face localization and tracking in the neural abstraction pyramid , 2005, Neural Computing & Applications.

[322]  Geoffrey E. Hinton,et al.  Reinforcement Learning with Factored States and Actions , 2004, J. Mach. Learn. Res..

[323]  Nuttapong Chentanez,et al.  Intrinsically Motivated Reinforcement Learning , 2004, NIPS.

[324]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[325]  Peter Stone,et al.  Policy gradient reinforcement learning for fast quadrupedal locomotion , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[326]  Nicole Immorlica,et al.  Locality-sensitive hashing scheme based on p-stable distributions , 2004, SCG '04.

[327]  Hani Hagras,et al.  Evolving spiking neural network controllers for autonomous robots , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[328]  Keechul Jung,et al.  GPU implementation of neural networks , 2004, Pattern Recognit..

[329]  Harald Haas,et al.  Harnessing Nonlinearity: Predicting Chaotic Systems and Saving Energy in Wireless Communication , 2004, Science.

[330]  G. Miller Learning to Forget , 2004, Science.

[331]  Chia-Feng Juang,et al.  A hybrid of genetic algorithm and particle swarm optimization for recurrent network design , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[332]  Jürgen Schmidhuber,et al.  Optimal Ordered Problem Solver , 2002, Machine Learning.

[333]  Nicolas Brunel,et al.  Dynamics of Sparsely Connected Networks of Excitatory and Inhibitory Spiking Neurons , 2000, Journal of Computational Neuroscience.

[334]  Jürgen Schmidhuber,et al.  Fast Online Q(λ) , 1998, Machine Learning.

[335]  Yoram Singer,et al.  The Hierarchical Hidden Markov Model: Analysis and Applications , 1998, Machine Learning.

[336]  Narendra Ahuja,et al.  Learning Recognition and Segmentation Using the Cresceptron , 1997, International Journal of Computer Vision.

[337]  Rich Caruana,et al.  Multitask Learning , 1997, Machine Learning.

[338]  Jürgen Schmidhuber,et al.  Shifting Inductive Bias with Success-Story Algorithm, Adaptive Levin Search, and Incremental Self-Improvement , 1997, Machine Learning.

[339]  Jing Peng,et al.  Incremental multi-step Q-learning , 1994, Machine Learning.

[340]  Jude W. Shavlik,et al.  Combining Symbolic and Neural Learning , 1994, Machine Learning.

[341]  Jude W. Shavlik,et al.  Using knowledge-based neural networks to improve algorithms: Refining the Chou-Fasman algorithm for protein folding , 2004, Machine Learning.

[342]  Jouko Lampinen,et al.  Clustering properties of hierarchical self-organizing maps , 1992, Journal of Mathematical Imaging and Vision.

[343]  Jude W. Shavlik,et al.  Using Knowledge-Based Neural Networks to Improve Algorithms: Refining the Chou–Fasman Algorithm for Protein Folding , 2004, Machine Learning.

[344]  Peter Dayan,et al.  Q-learning , 1992, Machine Learning.

[345]  Ronald J. Williams,et al.  Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.

[346]  Peter Dayan,et al.  Technical Note: Q-Learning , 2004, Machine Learning.

[347]  P. Földiák,et al.  Forming sparse representations by local anti-Hebbian learning , 1990, Biological Cybernetics.

[348]  R. Schapire The Strength of Weak Learnability , 1990, Machine Learning.

[349]  J. Stephen Judd A Reply to Honavar's Book Review of Neural Network Design and the Complexity of Learning , 2004, Machine Learning.

[350]  T. Kohonen,et al.  Self-organizing semantic maps , 1989, Biological Cybernetics.

[351]  A. K. Rigler,et al.  Accelerating the convergence of the back-propagation method , 1988, Biological Cybernetics.

[352]  Tom M. Mitchell,et al.  Explanation-Based Generalization: A Unifying View , 1986, Machine Learning.

[353]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[354]  L. Bobrowski Learning processes in multilayer threshold nets , 1978, Biological Cybernetics.

[355]  Stephen Grossberg,et al.  Adaptive pattern classification and universal recoding: II. Feedback, expectation, olfaction, illusions , 1976, Biological Cybernetics.

[356]  S. Grossberg,et al.  Adaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors , 1976, Biological Cybernetics.

[357]  Shinji Kusumoto,et al.  Biologically Inspired Approaches to Advanced Information Technology , 2004, Lecture Notes in Computer Science.

[358]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[359]  Isam Kaysi,et al.  IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS Editor , 2004 .

[360]  Christian W. Omlin,et al.  A Machine Learning Method for Extracting Symbolic Knowledge from Recurrent Neural Networks , 2004, Neural Computation.

[361]  Sridhar Mahadevan,et al.  Average reward reinforcement learning: Foundations, algorithms, and empirical results , 2004, Machine Learning.

[362]  Andrew W. Moore,et al.  Prioritized sweeping: Reinforcement learning with less data and less time , 2004, Machine Learning.

[363]  J. Rubner,et al.  Development of feature detectors by self-organization , 2004, Biological Cybernetics.

[364]  C. Malsburg Self-organization of orientation sensitive cells in the striate cortex , 2004, Kybernetik.

[365]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[366]  Aude Billard,et al.  From Animals to Animats , 2004 .

[367]  D. Perrett,et al.  Visual neurones responsive to faces in the monkey temporal cortex , 2004, Experimental Brain Research.

[368]  Nuttapong Chentanez,et al.  Intrinsically Motivated Learning of Hierarchical Collections of Skills , 2004 .

[369]  Alexander Gloye,et al.  FU-Fighters Small Size 2004 , 2004 .

[370]  R. Mooney,et al.  Explanation-Based Learning: An Alternative View , 1986, Machine Learning.

[371]  Steven J. Bradtke,et al.  Linear Least-Squares algorithms for temporal difference learning , 2004, Machine Learning.

[372]  Risto Miikkulainen,et al.  Efficient Reinforcement Learning through Symbiotic Evolution , 2004 .

[373]  Andrew W. Moore,et al.  The parti-game algorithm for variable resolution reinforcement learning in multidimensional state-spaces , 2004, Machine Learning.

[374]  H. Seung,et al.  Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission , 2003, Neuron.

[375]  Christian Igel,et al.  Neuroevolution for reinforcement learning using evolution strategies , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[376]  Jürgen Schmidhuber,et al.  A robot that reinforcement-learns to identify and memorize important previous observations , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[377]  Michail G. Lagoudakis,et al.  Least-Squares Policy Iteration , 2003, J. Mach. Learn. Res..

[378]  Pierre Baldi,et al.  The Principled Design of Large-Scale Recursive Neural Network Architectures--DAG-RNNs and the Protein Structure Prediction Problem , 2003, J. Mach. Learn. Res..

[379]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[380]  Sven Behnke,et al.  Hierarchical Neural Networks for Image Interpretation (Lecture Notes in Computer Science) , 2003 .

[381]  Mitsuo Kawato,et al.  Inter-module credit assignment in modular reinforcement learning , 2003, Neural Networks.

[382]  Stephan K. Chalup,et al.  Incremental training of first order recurrent neural networks to predict a context-sensitive language , 2003, Neural Networks.

[383]  Sven Behnke,et al.  Hierarchical Neural Networks for Image Interpretation , 2003, Lecture Notes in Computer Science.

[384]  Sridhar Mahadevan,et al.  Hierarchical Policy Gradient Algorithms , 2003, ICML.

[385]  Patrice Y. Simard,et al.  Best practices for convolutional neural networks applied to visual document analysis , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[386]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[387]  Sven Behnke,et al.  Discovering hierarchical speech features using convolutional non-negative matrix factorization , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[388]  Risto Miikkulainen,et al.  Active Guidance for a Finless Rocket Using Neuroevolution , 2003, GECCO.

[389]  Risto Miikkulainen,et al.  Evolving Keepaway Soccer Players through Task Decomposition , 2003, GECCO.

[390]  Dario Floreano,et al.  Hardware spiking neural network with run-time reconfigurable connectivity in an autonomous robot , 2003, NASA/DoD Conference on Evolvable Hardware, 2003. Proceedings..

[391]  Anne Condon,et al.  On the undecidability of probabilistic planning and related stochastic optimization problems , 2003, Artif. Intell..

[392]  Danil V. Prokhorov,et al.  Simple and conditioned adaptive behavior from Kalman filter trained recurrent networks , 2003, Neural Networks.

[393]  Douglas Aberdeen,et al.  Policy-Gradient Algorithms for Partially Observable Markov Decision Processes , 2003 .

[394]  Petros Koumoutsakos,et al.  Reducing the Time Complexity of the Derandomized Evolution Strategy with Covariance Matrix Adaptation (CMA-ES) , 2003, Evolutionary Computation.

[395]  Jürgen Schmidhuber,et al.  Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets , 2003, Neural Networks.

[396]  Peter Tiño,et al.  Architectural Bias in Recurrent Neural Networks: Fractal Analysis , 2002, Neural Computation.

[397]  Sridhar Mahadevan,et al.  Recent Advances in Hierarchical Reinforcement Learning , 2003, Discret. Event Dyn. Syst..

[398]  Julian F. Miller,et al.  Genetic and Evolutionary Computation — GECCO 2003 , 2003, Lecture Notes in Computer Science.

[399]  Risto Miikkulainen,et al.  Robust non-linear control through neuroevolution , 2003 .

[400]  Armando Vieira,et al.  A training algorithm for classification of high-dimensional data , 2003, Neurocomputing.

[401]  Christian Igel,et al.  Empirical evaluation of the improved Rprop learning algorithms , 2003, Neurocomputing.

[402]  Sridhar Mahadevan,et al.  Recent Advances in Hierarchical Reinforcement Learning , 2003, Discret. Event Dyn. Syst..

[403]  Jens Aage Hansen,et al.  New learning. , 2003, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.

[404]  Jürgen Schmidhuber,et al.  Learning Precise Timing with LSTM Recurrent Networks , 2003, J. Mach. Learn. Res..

[405]  Thomas Serre,et al.  On the Role of Object-Specific Features for Real World Object Recognition in Biological Vision , 2002, Biologically Motivated Computer Vision.

[406]  Jirí Síma,et al.  Training a Single Sigmoidal Neuron Is Hard , 2002, Neural Comput..

[407]  Henry Markram,et al.  Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations , 2002, Neural Computation.

[408]  Andreas Rauber,et al.  The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data , 2002, IEEE Trans. Neural Networks.

[409]  Sven Behnke,et al.  Learning Face Localization Using Hierarchical Recurrent Networks , 2002, ICANN.

[410]  Shie Mannor,et al.  Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning , 2002, ECML.

[411]  Danil V. Prokhorov,et al.  Adaptive behavior with fixed weights in RNN: an overview , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[412]  Paul E. Utgoff,et al.  Many-Layered Learning , 2002, Neural Computation.

[413]  Geoffrey E. Hinton Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[414]  Jürgen Schmidhuber,et al.  The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions , 2002, COLT.

[415]  Nicol N. Schraudolph,et al.  Fast Curvature Matrix-Vector Products for Second-Order Gradient Descent , 2002, Neural Computation.

[416]  Wulfram Gerstner,et al.  Spiking Neuron Models: An Introduction , 2002 .

[417]  Mitsuo Kawato,et al.  Multiple Model-Based Reinforcement Learning , 2002, Neural Computation.

[418]  Risto Miikkulainen,et al.  Evolving Neural Networks through Augmenting Topologies , 2002, Evolutionary Computation.

[419]  Terrence J. Sejnowski,et al.  Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.

[420]  Tao Zhang,et al.  Stable Adaptive Neural Network Control , 2001, The Springer International Series on Asian Studies in Computer and Information Science.

[421]  Ronen I. Brafman,et al.  R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning , 2001, J. Mach. Learn. Res..

[422]  Sander M. Bohte,et al.  Error-backpropagation in temporally encoded networks of spiking neurons , 2000, Neurocomputing.

[423]  Marcus Hutter The Fastest and Shortest Algorithm for all Well-Defined Problems , 2002, Int. J. Found. Comput. Sci..

[424]  J. van Leeuwen,et al.  Neural Networks: Tricks of the Trade , 2002, Lecture Notes in Computer Science.

[425]  Ofi rNw8x'pyzm,et al.  The Speed Prior: A New Simplicity Measure Yielding Near-Optimal Computable Predictions , 2002 .

[426]  Peter Deuflhard,et al.  Numerische Mathematik. I , 2002 .

[427]  Wulfram Gerstner,et al.  Spiking Neuron Models , 2002 .

[428]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[429]  Robert A. Legenstein,et al.  Neural circuits for pattern recognition with small total wire length , 2002, Theor. Comput. Sci..

[430]  Sven Behnke,et al.  Learning Iterative Image Reconstruction in the Neural Abstraction Pyramid , 2001, Int. J. Comput. Intell. Appl..

[431]  Jürgen Schmidhuber,et al.  LSTM recurrent networks learn simple context-free and context-sensitive languages , 2001, IEEE Trans. Neural Networks.

[432]  Dario Floreano,et al.  Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots , 2001, EvoRobots.

[433]  S. Haykin Kalman Filtering and Neural Networks , 2001 .

[434]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[435]  Jürgen Schmidhuber,et al.  Unsupervised Learning in LSTM Recurrent Neural Networks , 2001, ICANN.

[436]  Sepp Hochreiter,et al.  Learning to Learn Using Gradient Descent , 2001, ICANN.

[437]  Peter L. Bartlett,et al.  Infinite-Horizon Policy-Gradient Estimation , 2001, J. Artif. Intell. Res..

[438]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[439]  Jeremy Buhler,et al.  Efficient large-scale sequence comparison by locality-sensitive hashing , 2001, Bioinform..

[440]  John F. Kolen,et al.  Field Guide to Dynamical Recurrent Networks , 2001 .

[441]  Vivek S. Borkar,et al.  Learning Algorithms for Markov Decision Processes with Average Cost , 2001, SIAM J. Control. Optim..

[442]  Alfonso Valencia,et al.  A hierarchical unsupervised growing neural network for clustering gene expression patterns , 2001, Bioinform..

[443]  Tobi Delbrück,et al.  Orientation-Selective aVLSI Spiking Neurons , 2001, NIPS.

[444]  Bram Bakker,et al.  Reinforcement Learning with Long Short-Term Memory , 2001, NIPS.

[445]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[446]  Dongming Wang,et al.  LNCS: Lecture Notes In Computer Science , 2001 .

[447]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[448]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[449]  Jörg Rech,et al.  Knowledge Discovery in Databases , 2001, Künstliche Intell..

[450]  Herbert Jaeger,et al.  The''echo state''approach to analysing and training recurrent neural networks , 2001 .

[451]  Yoshua Bengio,et al.  Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .

[452]  Miguel Á. Carreira-Perpiñán,et al.  Continuous latent variable models for dimensionality reduction and sequential data reconstruction , 2001 .

[453]  Janet Wiles,et al.  Context-free and context-sensitive dynamics in recurrent neural networks , 2000, Connect. Sci..

[454]  Wolfgang Maass,et al.  On the Computational Power of Winner-Take-All , 2000, Neural Computation.

[455]  Jürgen Schmidhuber,et al.  Learning to Forget: Continual Prediction with LSTM , 2000, Neural Computation.

[456]  L. Abbott,et al.  Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.

[457]  John N. Tsitsiklis,et al.  A survey of computational complexity results in systems and control , 2000, Autom..

[458]  Jordan B. Pollack,et al.  RAAM for infinite context-free languages , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[459]  Andreas Rauber,et al.  The growing hierarchical self-organizing map , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[460]  Thomas G. Dietterich Ensemble Methods in Machine Learning , 2000, Multiple Classifier Systems.

[461]  J. Baxter,et al.  Direct gradient-based reinforcement learning , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[462]  Amir F. Atiya,et al.  New results on recurrent network training: unifying the algorithms and accelerating convergence , 2000, IEEE Trans. Neural Networks Learn. Syst..

[463]  Jihoon Yang,et al.  Constructive Neural-Network Learning Algorithms for Pattern Classification , 2000 .

[464]  Naonori Ueda,et al.  Optimal Linear Combination of Neural Networks for Improving Classification Performance , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[465]  Kaspar Anton Schindler,et al.  When pyramidal neurons lock, when they respond chaotically, and when they like to synchronize , 2000, Neuroscience Research.

[466]  Jürgen Schmidhuber,et al.  Recurrent nets that time and count , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[467]  Thomas G. Dietterich Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition , 1999, J. Artif. Intell. Res..

[468]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[469]  Stefan Sperlich,et al.  Generalized Additive Models , 2014 .

[470]  Gomes de Freitas,et al.  Bayesian methods for neural networks , 2000 .

[471]  Yishay Mansour,et al.  Policy Gradient Methods for Reinforcement Learning with Function Approximation , 1999, NIPS.

[472]  Giovanni Soda,et al.  Exploiting the past and the future in protein secondary structure prediction , 1999, Bioinform..

[473]  T. Poggio,et al.  Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.

[474]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[475]  Doina Precup,et al.  Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning , 1999, Artif. Intell..

[476]  Kee-Eung Kim,et al.  Learning Finite-State Controllers for Partially Observable Environments , 1999, UAI.

[477]  Sven Behnke Hebbian learning and competition in the neural abstraction pyramid , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[478]  F. Pasemann,et al.  Evolving structure and function of neurocontrollers , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[479]  Jürgen Schmidhuber,et al.  Feature Extraction Through LOCOCODE , 1999, Neural Computation.

[480]  Yoshinori Sagisaka,et al.  Phoneme boundary estimation using bidirectional recurrent neural networks and its applications , 1999, Systems and Computers in Japan.

[481]  R. Kempter,et al.  Hebbian learning and spiking neurons , 1999 .

[482]  Mike Schuster,et al.  On supervised learning from sequential data with applications for speech regognition , 1999 .

[483]  B. Schölkopf,et al.  Advances in kernel methods: support vector learning , 1999 .

[484]  Andrés Pérez Uribe,et al.  Structure-Adaptable Digital Neural Networks , 1999 .

[485]  Janet Wiles,et al.  Learning a context-free task with a recurrent neural network: An analysis of stability , 1999 .

[486]  C. P. D. Souto,et al.  The Loading Problem for Pyramidal Neural NetworksMarc , 1999 .

[487]  Paul Rodríguez,et al.  A Recurrent Neural Network that Learns to Count , 1999, Connect. Sci..

[488]  Andres Perez-Uribe,et al.  Structure-Adaptable Digital Neural Networks , 1999 .

[489]  Andrew W. Moore,et al.  Gradient Descent for General Reinforcement Learning , 1998, NIPS.

[490]  Erkki Oja,et al.  Sparse Code Shrinkage: Denoising by Nonlinear Maximum Likelihood Estimation , 1998, NIPS.

[491]  G. V. Puskorius,et al.  A signal processing framework based on dynamic neural networks with application to problems in adaptation, filtering, and classification , 1998, Proc. IEEE.

[492]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[493]  Peter Földiák,et al.  SPARSE CODING IN THE PRIMATE CORTEX , 2002 .

[494]  Tadashi Kondo,et al.  GMDH neural network algorithm using the heuristic self-organization method and its application to the pattern identification problem , 1998, Proceedings of the 37th SICE Annual Conference. International Session Papers.

[495]  Helko Lehmann,et al.  Computation in Recurrent Neural Networks: From Counters to Iterated Function Systems , 1998, Australian Joint Conference on Artificial Intelligence.

[496]  Henry Markram,et al.  Neural Networks with Dynamic Synapses , 1998, Neural Computation.

[497]  Sven Behnke,et al.  Neural abstraction pyramid: a hierarchical image understanding architecture , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[498]  Jürgen Schmidhuber,et al.  Reinforcement Learning with Self-Modifying Policies , 1998, Learning to Learn.

[499]  Leslie Pack Kaelbling,et al.  Planning and Acting in Partially Observable Stochastic Domains , 1998, Artif. Intell..

[500]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[501]  Shun-ichi Amari,et al.  Natural Gradient Works Efficiently in Learning , 1998, Neural Computation.

[502]  Danil V. Prokhorov,et al.  Enhanced Multi-Stream Kalman Filter Training for Recurrent Networks , 1998 .

[503]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[504]  Bruno A. Olshausen,et al.  Inferring Sparse, Overcomplete Image Codes Using an Efficient Coding Framework , 1998, NIPS.

[505]  Janet Wiles,et al.  Recurrent Neural Networks Can Learn to Implement Symbol-Sensitive Counting , 1997, NIPS.

[506]  Wofgang Maas,et al.  Networks of spiking neurons: the third generation of neural network models , 1997 .

[507]  Doina Precup,et al.  Multi-time Models for Temporally Abstract Planning , 1997, NIPS.

[508]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[509]  Kuldip K. Paliwal,et al.  Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..

[510]  M. W. Pedersen,et al.  Training recurrent networks , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[511]  William I. Gasarch,et al.  Book Review: An introduction to Kolmogorov Complexity and its Applications Second Edition, 1997 by Ming Li and Paul Vitanyi (Springer (Graduate Text Series)) , 1997, SIGACT News.

[512]  Jürgen Schmidhuber,et al.  HQ-Learning , 1997, Adapt. Behav..

[513]  Ashwin Ram,et al.  Experiments with Reinforcement Learning in Problems with Continuous State and Action Spaces , 1997, Adapt. Behav..

[514]  Geoffrey E. Hinton,et al.  Generative models for discovering sparse distributed representations. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[515]  Ian H. Witten,et al.  Stacked generalization: when does it work? , 1997, IJCAI 1997.

[516]  Shigenobu Kobayashi,et al.  Reinforcement Learning in POMDPs with Function Approximation , 1997, ICML.

[517]  Jürgen Schmidhuber,et al.  Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability , 1997, Neural Networks.

[518]  Jordan B. Pollack,et al.  Analysis of Dynamical Recognizers , 1997, Neural Computation.

[519]  Wulfram Gerstner,et al.  Reduction of the Hodgkin-Huxley Equations to a Single-Variable Threshold Model , 1997, Neural Computation.

[520]  Lucas C. Parra,et al.  Non-linear Feature Extraction by Redundancy Reduction in an Unsupervised Stochastic Neural Network , 1997, Neural Networks.

[521]  Rafal Salustowicz,et al.  Probabilistic Incremental Program Evolution , 1997, Evolutionary Computation.

[522]  Richard D. Braatz,et al.  On the "Identification and control of dynamical systems using neural networks" , 1997, IEEE Trans. Neural Networks.

[523]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[524]  Huaiyu Zhu On Information and Sufficiency , 1997 .

[525]  Kazumi Saito,et al.  Partial BFGS Update and Efficient Step-Length Calculation for Three-Layer Neural Networks , 1997, Neural Computation.

[526]  Jürgen Schmidhuber,et al.  Flat Minima , 1997, Neural Computation.

[527]  F. Waismann The Logical Calculus , 1997 .

[528]  Michael I. Jordan Serial Order: A Parallel Distributed Processing Approach , 1997 .

[529]  Nicolas Brunel,et al.  Dynamics of a recurrent network of spiking neurons before and following learning , 1997 .

[530]  David E. Moriarty,et al.  Symbiotic Evolution of Neural Networks in Sequential Decision Tasks , 1997 .

[531]  Hugo de Garis,et al.  "CBM (CAM-BRAIN MACHINE)" A Hardware Tool which Evolves a Neural Net Module in a Fraction of a Second and Runs a Million Neuron Artificial Brain in Real Time , 1997 .

[532]  Corso Elvezia Probabilistic Incremental Program Evolution , 1997 .

[533]  B. McNaughton,et al.  Population dynamics and theta rhythm phase precession of hippocampal place cell firing: A spiking neuron model , 1998, Hippocampus.

[534]  Thomas G. Dietterich What is machine learning? , 2020, Archives of Disease in Childhood.

[535]  Geoffrey E. Hinton,et al.  Varieties of Helmholtz Machine , 1996, Neural Networks.

[536]  Peter Tiño,et al.  Learning long-term dependencies in NARX recurrent neural networks , 1996, IEEE Trans. Neural Networks.

[537]  Pierre Baldi,et al.  Hybrid Modeling, HMM/NN Architectures, and Protein Applications , 1996, Neural Computation.

[538]  Craig Boutilier,et al.  Computing Optimal Policies for Partially Observable Decision Processes Using Compact Representations , 1996, AAAI/IAAI, Vol. 2.

[539]  Mike Casey,et al.  The Dynamics of Discrete-Time Computation, with Application to Recurrent Neural Networks and Finite State Machine Extraction , 1996, Neural Computation.

[540]  Larry D. Pyeatt,et al.  A comparison between cellular encoding and direct encoding for genetic neural networks , 1996 .

[541]  Luca Maria Gambardella,et al.  Learing Fine Motion by Using the Hierarchical Extended Kohonen Map , 1996, ICANN.

[542]  Randall C. O'Reilly,et al.  Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm , 1996, Neural Computation.

[543]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[544]  Davide Anguita,et al.  Mixing floating- and fixed-point formats for neural network learning on neuroprocessors , 1996, Microprocess. Microprogramming.

[545]  Jürgen Schmidhuber,et al.  Semilinear Predictability Minimization Produces Well-Known Feature Detectors , 1996, Neural Computation.

[546]  Andrew W. Moore,et al.  Reinforcement Learning: A Survey , 1996, J. Artif. Intell. Res..

[547]  Guozhong An,et al.  The Effects of Adding Noise During Backpropagation Training on a Generalization Performance , 1996, Neural Computation.

[548]  G. Orban,et al.  Model circuit of spiking neurons generating directional selectivity in simple cells. , 1996, Journal of neurophysiology.

[549]  urgen Schmidhuber The Neural Heat Exchanger , 1996 .

[550]  Jürgen Schmidhuber,et al.  Solving POMDPs with Levin Search and EIRA , 1996, ICML.

[551]  M. Stemmler A single spike suffices: the simplest form of stochastic resonance in model neurons , 1996 .

[552]  Maja J. Matarić,et al.  Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks , 1996 .

[553]  K S Narendra,et al.  Control of nonlinear dynamical systems using neural networks. II. Observability, identification, and control , 1996, IEEE Trans. Neural Networks.

[554]  Ralph Neuneier,et al.  How to Train Neural Networks , 1996, Neural Networks: Tricks of the Trade.

[555]  Wolfgang Maass,et al.  Lower Bounds for the Computational Power of Networks of Spiking Neurons , 1996, Neural Computation.

[556]  Lillian Lee,et al.  Learning of Context-Free Languages: A Survey of the Literature , 1996 .

[557]  Georg Dorffner,et al.  Neural Networks for Time Series Processing , 1996 .

[558]  Corso Elvezia Bridging Long Time Lags by Weight Guessing and \long Short Term Memory" , 1996 .

[559]  Michael L. Littman,et al.  Algorithms for Sequential Decision Making , 1996 .

[560]  Wolfgang Maass,et al.  Networks of Spiking Neurons: The Third Generation of Neural Network Models , 1996, Electron. Colloquium Comput. Complex..

[561]  C. Lee Giles,et al.  Extraction of rules from discrete-time recurrent neural networks , 1996, Neural Networks.

[562]  Andrew McCallum,et al.  Learning to Use Selective Attention and Short-Term Memory in Sequential Tasks , 1996 .

[563]  Mark Steijvers,et al.  A Recurrent Network that performs a Context-Sensitive Prediction Task , 1996 .

[564]  Leslie Pack Kaelbling,et al.  Learning Policies for Partially Observable Environments: Scaling Up , 1997, ICML.

[565]  Benjamin Van Roy,et al.  Feature-based methods for large scale dynamic programming , 1995, Proceedings of 1995 34th IEEE Conference on Decision and Control.

[566]  D. Signorini,et al.  Neural networks , 1995, The Lancet.

[567]  Joachim Diederich,et al.  Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..

[568]  R. Salama,et al.  Evolving neural network controllers , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[569]  Yoshua Bengio,et al.  Hierarchical Recurrent Neural Networks for Long-Term Dependencies , 1995, NIPS.

[570]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[571]  Terrence J. Sejnowski,et al.  Tempering Backpropagation Networks: Not All Weights are Created Equal , 1995, NIPS.

[572]  Ansgar Heinrich Ludolf West,et al.  Adaptive Back-Propagation in On-Line Learning of Multilayer Networks , 1995, NIPS.

[573]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[574]  Ben J. A. Kröse,et al.  Learning from delayed rewards , 1995, Robotics Auton. Syst..

[575]  Geoffrey E. Hinton,et al.  The Helmholtz Machine , 1995, Neural Computation.

[576]  Barak A. Pearlmutter Gradient calculations for dynamic recurrent neural networks: a survey , 1995, IEEE Trans. Neural Networks.

[577]  Jude W. Shavlik,et al.  Combining the Predictions of Multiple Classifiers: Using Competitive Learning to Initialize Neural Networks , 1995, IJCAI.

[578]  Leemon C. Baird,et al.  Residual Algorithms: Reinforcement Learning with Function Approximation , 1995, ICML.

[579]  Jürgen Schmidhuber Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability , 1995, ICML.

[580]  Peter Tiňo,et al.  Learning long-term dependencies is not as difficult with NARX recurrent neural networks , 1995 .

[581]  Kurt Hornik,et al.  Learning in linear neural networks: a survey , 1995, IEEE Trans. Neural Networks.

[582]  David B. Fogel,et al.  Evolving Neural Control Systems , 1995, IEEE Expert.

[583]  Juha Karhunen,et al.  Generalizations of principal component analysis, optimization problems, and neural networks , 1995, Neural Networks.

[584]  Geoffrey E. Hinton,et al.  The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.

[585]  Bruce W. Schmeiser,et al.  Improving model accuracy using optimal linear combinations of trained neural networks , 1995, IEEE Trans. Neural Networks.

[586]  Shixin Cheng,et al.  Dynamic learning rate optimization of the backpropagation algorithm , 1995, IEEE Trans. Neural Networks.

[587]  N. Logothetis,et al.  Shape representation in the inferior temporal cortex of monkeys , 1995, Current Biology.

[588]  R. Zemel,et al.  Learning sparse multiple cause models , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[589]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[590]  J. Stephen Judd,et al.  Optimal stopping and effective machine complexity in learning , 1993, Proceedings of 1995 IEEE International Symposium on Information Theory.

[591]  R. Tibshirani,et al.  Generalized additive models for medical research , 1995, Statistical methods in medical research.

[592]  Henry S. Baird,et al.  Document image defect models , 1995 .

[593]  Mark B. Ring Continual learning in reinforcement environments , 1995, GMD-Bericht.

[594]  Radford M. Neal Bayesian learning for neural networks , 1995 .

[595]  G. Stewart,et al.  Theory of the combination of observations least subject to error : part one, part two, supplement = Theoria combinationis observationum erroribus minimus obnoxiae : pars prior, pars posterior, supplementum , 1995 .

[596]  Pierre Baldi,et al.  Gradient descent learning algorithm overview: a general dynamical systems perspective , 1995, IEEE Trans. Neural Networks.

[597]  Rajesh Parekh,et al.  Constructive Neural Network Learning Algorithms for Multi-Category Pattern Classification , 1995 .

[598]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[599]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

[600]  Stefano Nolfi,et al.  Evolving Mobile Robots in Simulated and Real Environments , 1995, Artificial Life.

[601]  John Moody,et al.  Architecture Selection Strategies for Neural Networks: Application to Corporate Bond Rating Predicti , 1995, NIPS 1995.

[602]  Jan N. H. Heemskerk Overview of neural hardware , 1995 .

[603]  Janet Wiles,et al.  Learning to count without a counter: A case study of dynamics and activation landscapes in recurrent networks , 1995 .

[604]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[605]  Anton Gunzinger,et al.  Fast neural net simulation with a DSP processor array , 1995, IEEE Trans. Neural Networks.

[606]  Dario Floreano,et al.  From Animals to Animats 2: Proceedings of the Second International Conference on Simulation of Adaptive Behavior , 2000, Journal of Cognitive Neuroscience.

[607]  Panagiotis Manolios,et al.  First-Order Recurrent Neural Networks and Deterministic Finite State Automata , 1994, Neural Computation.

[608]  Reinhold Behringer,et al.  The seeing passenger car 'VaMoRs-P' , 1994, Proceedings of the Intelligent Vehicles '94 Symposium.

[609]  Christian Jacob,et al.  Genetic L-System Programming , 1994, PPSN.

[610]  Jude W. Shavlik,et al.  Knowledge-Based Artificial Neural Networks , 1994, Artif. Intell..

[611]  Jirí Síma,et al.  Loading Deep Networks Is Hard , 1994, Neural Comput..

[612]  Bart L. M. Happel,et al.  Design and evolution of modular neural network architectures , 1994, Neural Networks.

[613]  Astro Teller,et al.  The evolution of mental models , 1994 .

[614]  R. Vaillant,et al.  Original approach for the localisation of objects in images , 1994 .

[615]  Gerhard Weiß,et al.  Hierarchical Chunking in Classifier Systems , 1994, AAAI.

[616]  Satinder P. Singh,et al.  Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes , 1994, AAAI.

[617]  Karl Sims,et al.  Evolving virtual creatures , 1994, SIGGRAPH.

[618]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[619]  L. C. Baird,et al.  Reinforcement learning in continuous time: advantage updating , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[620]  Schuster,et al.  Separation of a mixture of independent signals using time delayed correlations. , 1994, Physical review letters.

[621]  Shumeet Baluja,et al.  A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning , 1994 .

[622]  Jeffrey L. Elman,et al.  Learning and Evolution in Neural Networks , 1994, Adapt. Behav..

[623]  Ali A. Minai,et al.  Perturbation response in feedforward networks , 1994, Neural Networks.

[624]  Yves Deville,et al.  Logic Program Synthesis , 1994, J. Log. Program..

[625]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[626]  G. Kane Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol 1: Foundations, vol 2: Psychological and Biological Models , 1994 .

[627]  Gerald Tesauro,et al.  TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play , 1994, Neural Computation.

[628]  Lee A. Feldkamp,et al.  Neurocontrol of nonlinear dynamical systems with Kalman filter trained recurrent networks , 1994, IEEE Trans. Neural Networks.

[629]  Neil Burgess,et al.  A Constructive Algorithm that Converges for Real-Valued Input Patterns , 1994, Int. J. Neural Syst..

[630]  Keiji Tanaka,et al.  Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. , 1994, Journal of neurophysiology.

[631]  Bernard Widrow,et al.  Neural networks: applications in industry, business and science , 1994, CACM.

[632]  Les E. Atlas,et al.  Recurrent neural networks and robust time series prediction , 1994, IEEE Trans. Neural Networks.

[633]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[634]  Padhraic Smyth,et al.  Discrete recurrent neural networks for grammatical inference , 1994, IEEE Trans. Neural Networks.

[635]  Davide Anguita,et al.  An efficient implementation of BP on RISC-based workstations , 1994, Neurocomputing.

[636]  Achilleas Zapranis,et al.  Stock performance modeling using neural networks: A comparative study with regression models , 1994, Neural Networks.

[637]  Barak A. Pearlmutter Fast Exact Multiplication by the Hessian , 1994, Neural Computation.

[638]  KD Miller A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity-dependent competition between ON- and OFF-center inputs , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[639]  Randall D. Beer,et al.  Sequential Behavior and Learning in Evolved Dynamical Neural Networks , 1994, Adapt. Behav..

[640]  J. Nadal,et al.  Nonlinear neurons in the low-noise limit: a factorial code maximizes information transfer Network 5 , 1994 .

[641]  S. S. Viglione,et al.  Applications of pattern recognition technology , 1994 .

[642]  A. Jefferson Offutt,et al.  An Empirical Evaluation , 1994 .

[643]  Vittorio Maniezzo,et al.  Genetic evolution of the topology and weight distribution of neural networks , 1994, IEEE Trans. Neural Networks.

[644]  J. Nadal Non linear neurons in the low noise limit : a factorial code maximizes information transferJean , 1994 .

[645]  Mahesan Niranjan,et al.  On-line Q-learning using connectionist systems , 1994 .

[646]  Stefano Nolfi,et al.  How to Evolve Autonomous Robots: Different Approaches in Evolutionary Robotics , 1994 .

[647]  J. Cardoso On the Performance of Orthogonal Source Separation Algorithms , 1994 .

[648]  R. Zemel A minimum description length framework for unsupervised learning , 1994 .

[649]  C. Lee Giles,et al.  Effects of Noise on Convergence and Generalization in Recurrent Networks , 1994, NIPS.

[650]  Michael I. Jordan,et al.  Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems , 1994, NIPS.

[651]  Bernd Fritzke,et al.  A Growing Neural Gas Network Learns Topologies , 1994, NIPS.

[652]  Eric Saund,et al.  Unsupervised Learning of Mixtures of Multiple Causes in Binary Data , 1993, NIPS.

[653]  David H. Wolpert,et al.  Bayesian Backpropagation Over I-O Functions Rather Than Weights , 1993, NIPS.

[654]  John E. Moody,et al.  Fast Pruning Using Principal Components , 1993, NIPS.

[655]  Hervé Bourlard,et al.  Connectionist Speech Recognition: A Hybrid Approach , 1993 .

[656]  Geoffrey E. Hinton,et al.  Keeping Neural Networks Simple , 1993 .

[657]  Reinhard Männer,et al.  Multiprocessor And Memory Architecture Of The Neurocomputer Synapse-1 , 1993, Int. J. Neural Syst..

[658]  Christopher M. Bishop,et al.  Curvature-driven smoothing: a learning algorithm for feedforward networks , 1993, IEEE Trans. Neural Networks.

[659]  Jonas Karlsson,et al.  Learning via task decomposition , 1993 .

[660]  Jürgen Schmidhuber,et al.  Planning simple trajectories using neural subgoal generators , 1993 .

[661]  D. Zipser,et al.  A spiking network model of short-term active memory , 1993, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[662]  Yann LeCun,et al.  Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..

[663]  Mitsuo Kawato,et al.  Neural network control for a closed-loop System using Feedback-error-learning , 1993, Neural Networks.

[664]  Jürgen Schmidhuber,et al.  Discovering Predictable Classifications , 1993, Neural Computation.

[665]  Anton Schwartz,et al.  A Reinforcement Learning Method for Maximizing Undiscounted Rewards , 1993, ICML.

[666]  M. A. Andrade,et al.  Evaluation of secondary structure of proteins from UV circular dichroism spectra using an unsupervised learning neural network. , 1993, Protein engineering.

[667]  J. Schmidhuber An 'introspective' network that can learn to run its own weight change algorithm , 1993 .

[668]  R. Vaillant,et al.  An original approach for the localization of objects in images , 1993 .

[669]  F. Vallet,et al.  Robustness in Multilayer Perceptrons , 1993, Neural Computation.

[670]  Pierre Baldi,et al.  Neural Networks for Fingerprint Recognition , 1993, Neural Computation.

[671]  Vasant Honavar,et al.  Generative learning structures and processes for generalized connectionist networks , 1993, Inf. Sci..

[672]  Martin A. Riedmiller,et al.  A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.

[673]  A. S. Weigend,et al.  Results of the time series prediction competition at the Santa Fe Institute , 1993, IEEE International Conference on Neural Networks.

[674]  A. Norman Redlich,et al.  Redundancy Reduction as a Strategy for Unsupervised Learning , 1993, Neural Computation.

[675]  Kumpati S. Narendra,et al.  Control of nonlinear dynamical systems using neural networks: controllability and stabilization , 1993, IEEE Trans. Neural Networks.

[676]  M. F. Møller,et al.  Exact Calculation of the Product of the Hessian Matrix of Feed-Forward Network Error Functions and a Vector in 0(N) Time , 1993 .

[677]  Stefanie N. Lindstaedt,et al.  Comparison of two Unsupervised Neural Network Models for Redundancy Reduction , 1993 .

[678]  Jürgen Schmidhuber,et al.  Netzwerkarchitekturen, Zielfunktionen und Kettenregel , 1993 .

[679]  Inman Harvey,et al.  Evolving Recurrent Dynamical Networks for Robot Control , 1993 .

[680]  Sean B. Holden,et al.  On the theory of generalization and self-structuring in linearly weighted connectionist networks , 1993 .

[681]  Xin Yao,et al.  A review of evolutionary artificial neural networks , 1993, Int. J. Intell. Syst..

[682]  Shun-ichi Amari,et al.  Statistical Theory of Learning Curves under Entropic Loss Criterion , 1993, Neural Computation.

[683]  Frank Bärmann,et al.  A learning algorithm for multilayered neural networks based on linear least squares problems , 1993, Neural Networks.

[684]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[685]  Andrzej Cichocki,et al.  Neural networks for optimization and signal processing , 1993 .

[686]  Ming Li,et al.  An Introduction to Kolmogorov Complexity and Its Applications , 2019, Texts in Computer Science.

[687]  Eduardo D. Sontag,et al.  Neural Networks for Control , 1993 .

[688]  Jürgen Schmidhuber,et al.  Continuous history compression , 1993 .

[689]  Eric A. Wan,et al.  Time series prediction by using a connectionist network with internal delay lines , 1993 .

[690]  Geoffrey E. Hinton,et al.  Developing Population Codes by Minimizing Description Length , 1993, NIPS.

[691]  Osamu Watanabe,et al.  Kolmogorov Complexity and Computational Complexity , 2012, EATCS Monographs on Theoretical Computer Science.

[692]  Tony Plate,et al.  Holographic Recurrent Networks , 1992, NIPS.

[693]  Mark B. Ring Learning Sequential Tasks by Incrementally Adding Higher Orders , 1992, NIPS.

[694]  Alan F. Murray,et al.  Synaptic Weight Noise During MLP Learning Enhances Fault-Tolerance, Generalization and Learning Trajectory , 1992, NIPS.

[695]  Guo-Zheng Sun,et al.  Time Warping Invariant Neural Networks , 1992, NIPS.

[696]  Barak A. Pearlmutter,et al.  Automatic Learning Rate Maximization by On-Line Estimation of the Hessian's Eigenvectors , 1992, NIPS 1992.

[697]  Babak Hassibi,et al.  Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.

[698]  Garrison W. Cottrell,et al.  Non-Linear Dimensionality Reduction , 1992, NIPS.

[699]  Geoffrey E. Hinton,et al.  Feudal Reinforcement Learning , 1992, NIPS.

[700]  Gert Cauwenberghs,et al.  A Fast Stochastic Error-Descent Algorithm for Supervised Learning and Optimization , 1992, NIPS.

[701]  Terrence J. Sejnowski,et al.  Unsupervised Discrimination of Clustered Data via Optimization of Binary Information Gain , 1992, NIPS.

[702]  Günther Palm,et al.  On the Information Storage Capacity of Local Learning Rules , 1992, Neural Computation.

[703]  E. Capaldi,et al.  The organization of behavior. , 1992, Journal of applied behavior analysis.

[704]  Schuster Hg Learning by maximizing the information transfer through nonlinear noisy neurons and "noise breakdown , 1992 .

[705]  Geoffrey E. Hinton,et al.  Simplifying Neural Networks by Soft Weight-Sharing , 1992, Neural Computation.

[706]  Zhaoping Li,et al.  Understanding Retinal Color Coding from First Principles , 1992, Neural Computation.

[707]  Michael I. Jordan,et al.  Forward Models: Supervised Learning with a Distal Teacher , 1992, Cogn. Sci..

[708]  Narendra Ahuja,et al.  Cresceptron: a self-organizing neural network which grows adaptively , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[709]  Ronald J. Williams,et al.  Training recurrent networks using the extended Kalman filter , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[710]  F. Faggin,et al.  Neural network hardware , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[711]  Donald A. Sofge,et al.  Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches , 1992 .

[712]  Kiyotoshi Matsuoka,et al.  Noise injection into inputs in back-propagation learning , 1992, IEEE Trans. Syst. Man Cybern..

[713]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[714]  E. Allender Applications of Time-Bounded Kolmogorov Complexity in Complexity Theory , 1992 .

[715]  Roberto Battiti,et al.  First- and Second-Order Methods for Learning: Between Steepest Descent and Newton's Method , 1992, Neural Computation.

[716]  Jürgen Schmidhuber,et al.  Learning Complex, Extended Sequences Using the Principle of History Compression , 1992, Neural Computation.

[717]  Jürgen Schmidhuber,et al.  A Fixed Size Storage O(n3) Time Complexity Learning Algorithm for Fully Recurrent Continually Running Networks , 1992, Neural Computation.

[718]  David H. Wolpert,et al.  Stacked generalization , 1992, Neural Networks.

[719]  D I Perrett,et al.  Organization and functions of cells responsive to faces in the temporal cortex. , 1992, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[720]  Elie Bienenstock,et al.  Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.

[721]  Schuster Learning by maximizing the information transfer through nonlinear noisy neurons and "noise breakdown" , 1992, Physical review. A, Atomic, molecular, and optical physics.

[722]  Long-Ji Lin,et al.  Reinforcement learning for robots using neural networks , 1992 .

[723]  Steven Douglas Whitehead,et al.  Reinforcement learning for the adaptive control of perception and action , 1992 .

[724]  George W. Irwin,et al.  Neural networks for control and systems , 1992 .

[725]  Chalapathy Neti,et al.  Maximally fault tolerant neural networks , 1992, IEEE Trans. Neural Networks.

[726]  Wulfram Gerstner,et al.  Associative memory in a network of ‘spiking’ neurons , 1992 .

[727]  J. Urgen Schmidhuber,et al.  Learning Factorial Codes by Predictability Minimization , 1992, Neural Computation.

[728]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[729]  John E. Moody,et al.  The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems , 1991, NIPS.

[730]  Vladimir Vapnik,et al.  Principles of Risk Minimization for Learning Theory , 1991, NIPS.

[731]  Raymond L. Watrous,et al.  Induction of Finite-State Automata Using Second-Order Recurrent Networks , 1991, NIPS.

[732]  Isabelle Guyon,et al.  Structural Risk Minimization for Character Recognition , 1991, NIPS.

[733]  Anders Krogh,et al.  A Simple Weight Decay Can Improve Generalization , 1991, NIPS.

[734]  Michael C. Mozer,et al.  Induction of Multiscale Temporal Structure , 1991, NIPS.

[735]  William Frawley,et al.  Knowledge Discovery in Databases , 1991 .

[736]  Jürgen Schmidhuber,et al.  Curious model-building control systems , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[737]  Christian Jutten,et al.  Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture , 1991, Signal Process..

[738]  A. P. Wieland,et al.  Evolving neural network controllers for unstable systems , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[739]  T. Bayes An essay towards solving a problem in the doctrine of chances , 2003 .

[740]  Sukhan Lee,et al.  A Gaussian potential function network with hierarchically self-organizing learning , 1991, Neural Networks.

[741]  M. Kramer Nonlinear principal component analysis using autoassociative neural networks , 1991 .

[742]  Jürgen Schmidhuber,et al.  Learning to generate sub-goals for action sequences , 1991 .

[743]  Sepp Hochreiter,et al.  Untersuchungen zu dynamischen neuronalen Netzen , 1991 .

[744]  Yoshua Bengio,et al.  Artificial neural networks and their application to sequence recognition , 1991 .

[745]  Wray L. Buntine,et al.  Bayesian Back-Propagation , 1991, Complex Syst..

[746]  Suzanna Becker,et al.  Unsupervised Learning Procedures for Neural Networks , 1991, Int. J. Neural Syst..

[747]  D. J. Felleman,et al.  Distributed hierarchical processing in the primate cerebral cortex. , 1991, Cerebral cortex.

[748]  Eduardo Sontag,et al.  Turing computability with neural nets , 1991 .

[749]  D. Mackay,et al.  A Practical Bayesian Framework for Backprop Networks , 1991 .

[750]  Jürgen Schmidhuber,et al.  Learning to Generate Artificial Fovea Trajectories for Target Detection , 1991, Int. J. Neural Syst..

[751]  Jing Peng,et al.  An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories , 1990, Neural Computation.

[752]  M. Mozer Discovering Discrete Distributed Representations with Iterative Competitive Learning , 1990, NIPS 1990.

[753]  Y. Le Cun,et al.  VLSI implementations of electronic neural networks: an example in character recognition , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[754]  Jordan B. Pollack,et al.  Recursive Distributed Representations , 1990, Artif. Intell..

[755]  Alexander H. Waibel,et al.  The Tempo 2 Algorithm: Adjusting Time-Delays By Supervised Learning , 1990, NIPS.

[756]  José Carlos Príncipe,et al.  A Theory for Neural Networks with Time Delays , 1990, NIPS.

[757]  Jürgen Schmidhuber,et al.  Reinforcement Learning in Markovian and Non-Markovian Environments , 1990, NIPS.

[758]  David E. Rumelhart,et al.  Generalization by Weight-Elimination with Application to Forecasting , 1990, NIPS.

[759]  Scott E. Fahlman,et al.  The Recurrent Cascade-Correlation Architecture , 1990, NIPS.

[760]  Pasi Koikkalainen,et al.  Self-organizing hierarchical feature maps , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[761]  Jürgen Schmidhuber,et al.  An on-line algorithm for dynamic reinforcement learning and planning in reactive environments , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[762]  Stephen José Hanson,et al.  A stochastic version of the delta rule , 1990 .

[763]  U. García-Palomares A class of methods for solving large convex systems , 1990 .

[764]  P Perona,et al.  Preattentive texture discrimination with early vision mechanisms. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[765]  Helge J. Ritter,et al.  Three-dimensional neural net for learning visuomotor coordination of a robot arm , 1990, IEEE Trans. Neural Networks.

[766]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[767]  L. B. Almeida A learning rule for asynchronous perceptrons with feedback in a combinatorial environment , 1990 .

[768]  Jürgen Schmidhuber,et al.  Dynamische neuronale Netze und das fundamentale raumzeitliche Lernproblem , 1990 .

[769]  Hiroaki Kitano,et al.  Designing Neural Networks Using Genetic Algorithms with Graph Generation System , 1990, Complex Syst..

[770]  Geoffrey E. Hinton,et al.  A time-delay neural network architecture for isolated word recognition , 1990, Neural Networks.

[771]  D. Mackay,et al.  Analysis of Linsker's application of Hebbian rules to linear networks , 1990 .

[772]  D. T. Wang,et al.  Structured Document Image Analysis, IAPR Workshop on Syntactic and Structural Pattern Recognition, 13-15 June 1990, Murray Hill, NJ, USA , 1990 .

[773]  Bernard Widrow,et al.  THE TRUCK BACKER-UPPER , 1990 .

[774]  J. Rubner,et al.  Development of feature detectors by self-organization. A network model. , 1990, Biological cybernetics.

[775]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1990, Bulletin of mathematical biology.

[776]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[777]  David J. C. MacKay,et al.  Analysis of Linsker's Simulations of Hebbian Rules , 1990, Neural Computation.

[778]  T. Sejnowski,et al.  Learning Algorithms for Networks with Internal and External Feedback , 1990 .

[779]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[780]  Bart Kosko,et al.  Unsupervised learning in noise , 1990, International 1989 Joint Conference on Neural Networks.

[781]  Paul J. Werbos,et al.  Neural networks for control and system identification , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[782]  Halbert White,et al.  Learning in Artificial Neural Networks: A Statistical Perspective , 1989, Neural Computation.

[783]  Lawrence D. Jackel,et al.  Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.

[784]  H. B. Barlow,et al.  Finding Minimum Entropy Codes , 1989, Neural Computation.

[785]  Oren Etzioni,et al.  Explanation-Based Learning: A Problem Solving Perspective , 1989, Artif. Intell..

[786]  Geoffrey E. Hinton Connectionist Learning Procedures , 1989, Artif. Intell..

[787]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.

[788]  B. Yandell Spline smoothing and nonparametric regression , 1989 .

[789]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[790]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[791]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[792]  Teuvo Kohonen,et al.  Self-organization and associative memory: 3rd edition , 1989 .

[793]  Barak A. Pearlmutter Learning State Space Trajectories in Recurrent Neural Networks , 1989, Neural Computation.

[794]  L. Steels,et al.  Accelerated Learning in Back-propagation Nets , 1989 .

[795]  Jude Shavlik,et al.  Combining Explanation-Based and Neural Learning: An Algorithm and Empirical Results , 1989 .

[796]  Jude Shavlik,et al.  An Approach to Combining Explanation-based and Neural Learning Algorithms , 1989 .

[797]  Lawrence D. Jackel,et al.  Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.

[798]  Jürgen Schmidhuber,et al.  A local learning algorithm for dynamic feedforward and recurrent networks , 1990, Forschungsberichte, TU Munich.

[799]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[800]  Ronald J. Williams,et al.  Experimental Analysis of the Real-time Recurrent Learning Algorithm , 1989 .

[801]  A. Teolis Adaptive Pattern Classification , 1989 .

[802]  Frank Fallside,et al.  Dynamic reinforcement driven error propagation networks with application to game playing , 1989 .

[803]  Yann LeCun,et al.  Improving the convergence of back-propagation learning with second-order methods , 1989 .

[804]  J. Stoer Numerische Mathematik 1 , 1989 .

[805]  M. C. Jones,et al.  Spline Smoothing and Nonparametric Regression. , 1989 .

[806]  Erkki Oja,et al.  Neural Networks, Principal Components, and Subspaces , 1989, Int. J. Neural Syst..

[807]  B. Widrow,et al.  The truck backer-upper: an example of self-learning in neural networks , 1989, International 1989 Joint Conference on Neural Networks.

[808]  T. Ash,et al.  Dynamic node creation in backpropagation networks , 1989, International 1989 Joint Conference on Neural Networks.

[809]  M. Gherrity,et al.  A learning algorithm for analog, fully recurrent neural networks , 1989, International 1989 Joint Conference on Neural Networks.

[810]  P. J. Werbos,et al.  Backpropagation and neurocontrol: a review and prospectus , 1989, International 1989 Joint Conference on Neural Networks.

[811]  Robert Hecht-Nielsen,et al.  Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.

[812]  Michael J. Carter,et al.  Operational Fault Tolerance of CMAC Networks , 1989, NIPS.

[813]  Roberto Battiti,et al.  Accelerated Backpropagation Learning: Two Optimization Methods , 1989, Complex Syst..

[814]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[815]  Richard Rohwer,et al.  The "Moving Targets" Training Algorithm , 1989, NIPS.

[816]  Kurt Hornik,et al.  Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.

[817]  Yann LeCun,et al.  Optimal Brain Damage , 1989, NIPS.

[818]  Esther Levin,et al.  Accelerated Learning in Layered Neural Networks , 1988, Complex Syst..

[819]  Ronald L. Rivest,et al.  Training a 3-node neural network is NP-complete , 1988, COLT '88.

[820]  Michael I. Jordan Supervised learning and systems with excess degrees of freedom , 1988 .

[821]  Ralph Linsker,et al.  Self-organization in a perceptual network , 1988, Computer.

[822]  Stephen I. Gallant,et al.  Connectionist expert systems , 1988, CACM.

[823]  Robert A. Jacobs,et al.  Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.

[824]  Jordan B. Pollack,et al.  Implications of Recursive Distributed Representations , 1988, NIPS.

[825]  John E. Moody,et al.  Fast Learning in Multi-Resolution Hierarchies , 1988, NIPS.

[826]  Michael C. Mozer,et al.  Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment , 1988, NIPS.

[827]  Alan J. Gross,et al.  Self-Organizing Methods in Modeling , 1988 .

[828]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[829]  Petre Stoica,et al.  Decentralized Control , 2018, The Control Systems Handbook.

[830]  PAUL J. WERBOS,et al.  Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.

[831]  Terence D. Sanger,et al.  An Optimality Principle for Unsupervised Learning , 1988, NIPS.

[832]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[833]  Lorien Y. Pratt,et al.  Comparing Biases for Minimal Network Construction with Back-Propagation , 1988, NIPS.

[834]  Yann Le Cun,et al.  A Theoretical Framework for Back-Propagation , 1988 .

[835]  Scott E. Fahlman,et al.  An empirical study of learning speed in back-propagation networks , 1988 .

[836]  James L. McClelland,et al.  James L. McClelland, David Rumelhart and the PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition . Vol. 1. Foundations . Vol. 2. Psychological and biological models . Cambridge MA: M.I.T. Press, 1987. , 1989, Journal of Child Language.

[837]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[838]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[839]  Pineda,et al.  Generalization of back-propagation to recurrent neural networks. , 1987, Physical review letters.

[840]  Dana H. Ballard,et al.  Modular Learning in Neural Networks , 1987, AAAI.

[841]  Barak A. Pearlmutter,et al.  G-maximization: An unsupervised learning procedure for discovering regularities , 1987 .

[842]  Paul J. Werbos,et al.  Building and Understanding Adaptive Systems: A Statistical/Numerical Approach to Factory Automation and Brain Research , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[843]  J. Austin Associative memory , 1987 .

[844]  Alan S. Lapedes,et al.  A self-optimizing, nonsymmetrical neural net for content addressable memory and pattern recognition , 1986 .

[845]  J. Rissanen Stochastic Complexity and Modeling , 1986 .

[846]  Elliot Soloway,et al.  Learning to program = learning to construct mechanisms and explanations , 1986, CACM.

[847]  Geoffrey E. Hinton,et al.  Learning and relearning in Boltzmann machines , 1986 .

[848]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[849]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .

[850]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[851]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..

[852]  Robert Balzer,et al.  A 15 Year Perspective on Automatic Programming , 1985, IEEE Transactions on Software Engineering.

[853]  Satosi Watanabe,et al.  Pattern Recognition: Human and Mechanical , 1985 .

[854]  Nichael Lynn Cramer,et al.  A Representation for the Adaptive Generation of Simple Sequential Programs , 1985, ICGA.

[855]  M. Hutchinson,et al.  Smoothing noisy data with spline functions , 1985 .

[856]  Yann LeCun,et al.  Une procedure d'apprentissage pour reseau a seuil asymmetrique (A learning scheme for asymmetric threshold networks) , 1985 .

[857]  R. Desimone,et al.  Stimulus-selective properties of inferior temporal neurons in the macaque , 1984, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[858]  Stanley J. Farlow,et al.  Self-Organizing Methods in Modeling: Gmdh Type Algorithms , 1984 .

[859]  O. Firschein,et al.  Syntactic pattern recognition and applications , 1983, Proceedings of the IEEE.

[860]  Richard S. Sutton,et al.  Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[861]  Douglas B. Lenat,et al.  Why AM and EURISKO Appear to Work , 1984, Artif. Intell..

[862]  Douglas B. Lenat,et al.  Theory Formation by Heuristic Search , 1983, Artificial Intelligence.

[863]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[864]  T. Kohonen Self-organized formation of topographically correct feature maps , 1982 .

[865]  Paul J. Werbos,et al.  Applications of advances in nonlinear sensitivity analysis , 1982 .

[866]  Jan de Leeuw,et al.  Nonlinear Principal Component Analysis , 1982 .

[867]  Nils J. Nilsson,et al.  Principles of Artificial Intelligence , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[868]  Hans-Paul Schwefel,et al.  Numerical Optimization of Computer Models , 1982 .

[869]  Temple F. Smith Occam's razor , 1980, Nature.

[870]  B. Speelpenning Compiling Fast Partial Derivatives of Functions Given by Algorithms , 1980 .

[871]  Stephen F. Smith,et al.  A learning system based on genetic adaptive algorithms , 1980 .

[872]  George M. Siouris,et al.  Applied Optimal Control: Optimization, Estimation, and Control , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[873]  Ray J. Solomonoff,et al.  Complexity-based induction systems: Comparisons and convergence theorems , 1978, IEEE Trans. Inf. Theory.

[874]  Roman Bek,et al.  Discourse on one way in which a quantum-mechanics language on the classical logical base can be built up , 1978, Kybernetika.

[875]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[876]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[877]  C. Malsburg,et al.  How patterned neural connections can be set up by self-organization , 1976, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[878]  S. Linnainmaa Taylor expansion of the accumulated rounding error , 1976 .

[879]  Saburo Ikeda,et al.  Sequential GMDH Algorithm and Its Application to River Flow Prediction , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[880]  G. Wahba Smoothing noisy data with spline functions , 1975 .

[881]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[882]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[883]  M. Stone Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .

[884]  H. Akaike A new look at the statistical model identification , 1974 .

[885]  Kumpati S. Narendra,et al.  Learning Automata - A Survey , 1974, IEEE Trans. Syst. Man Cybern..

[886]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[887]  S. Dreyfus The computational solution of optimal control problems with time lag , 1973 .

[888]  H. Akaike,et al.  Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .

[889]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[890]  Kumpati S. Narendra,et al.  Adaptive, learning, and pattern recognition systems: Theory and applications , 1972 .

[891]  Teuvo Kohonen,et al.  Correlation Matrix Memories , 1972, IEEE Transactions on Computers.

[892]  R. Cox,et al.  Journal of the Royal Statistical Society B , 1972 .

[893]  A. G. Ivakhnenko,et al.  Polynomial Theory of Complex Systems , 1971, IEEE Trans. Syst. Man Cybern..

[894]  Stephen A. Cook,et al.  The complexity of theorem-proving procedures , 1971, STOC.

[895]  H. Akaike Statistical predictor identification , 1970 .

[896]  D. Shanno Conditioning of Quasi-Newton Methods for Function Minimization , 1970 .

[897]  Adrien-Marie Legendre,et al.  Nouvelles méthodes pour la détermination des orbites des comètes , 1970 .

[898]  marquis de L'Hospital Analyse des infiniment petits, pour l'intelligence des lignes courbes , 1970 .

[899]  D. Goldfarb A family of variable-metric methods derived by variational means , 1970 .

[900]  R. Rohrer,et al.  Automated Network Design-The Frequency-Domain Case , 1969 .

[901]  Richard C. T. Lee,et al.  PROW: A Step Toward Automatic Program Writing , 1969, IJCAI.

[902]  S. Grossberg Some Networks That Can Learn, Remember, and Reproduce any Number of Complicated Space-Time Patterns, I , 1969 .

[903]  C. S. Wallace,et al.  An Information Measure for Classification , 1968, Comput. J..

[904]  A. Lindenmayer Mathematical models for cellular interactions in development. I. Filaments with one-sided inputs. , 1968, Journal of theoretical biology.

[905]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[906]  Aristid Lindenmayer,et al.  Mathematical Models for Cellular Interactions in Development , 1968 .

[907]  A. Kolmogorov Three approaches to the quantitative definition of information , 1968 .

[908]  Shun-ichi Amari,et al.  A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..

[909]  Alekseĭ Grigorʹevich Ivakhnenko,et al.  Cybernetics and forecasting techniques , 1967 .

[910]  L. Baum,et al.  Statistical Inference for Probabilistic Functions of Finite State Markov Chains , 1966 .

[911]  Gregory J. Chaitin,et al.  On the Length of Programs for Computing Finite Binary Sequences , 1966, JACM.

[912]  Alekseĭ Grigorʹevich Ivakhnenko,et al.  CYBERNETIC PREDICTING DEVICES , 1966 .

[913]  R. Bellman Dynamic Programming , 1957, Science.

[914]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[915]  J. H. Wilkinson The algebraic eigenvalue problem , 1966 .

[916]  M. L. Chambers The Mathematical Theory of Optimal Processes , 1965 .

[917]  C. G. Broyden A Class of Methods for Solving Nonlinear Simultaneous Equations , 1965 .

[918]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part II , 1964, Inf. Control..

[919]  Ray J. Solomonoff,et al.  A Formal Theory of Inductive Inference. Part I , 1964, Inf. Control..

[920]  E. Blum,et al.  The Mathematical Theory of Optimal Processes. , 1963 .

[921]  Roger Fletcher,et al.  A Rapidly Convergent Descent Method for Minimization , 1963, Comput. J..

[922]  D. Marquardt An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[923]  L. S. Pontryagin,et al.  Mathematical Theory of Optimal Processes , 1962 .

[924]  S. Yoshizawa,et al.  An Active Pulse Transmission Line Simulating Nerve Axon , 1962, Proceedings of the IRE.

[925]  S. Dreyfus The numerical solution of variational problems , 1962 .

[926]  A. E. Bryson,et al.  A Steepest-Ascent Method for Solving Optimum Programming Problems , 1962 .

[927]  A. A. Mullin,et al.  Principles of neurodynamics , 1962 .

[928]  Bernard Widrow,et al.  Associative Storage and Retrieval of Digital Information in Networks of Adaptive “Neurons” , 1962 .

[929]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[930]  Marvin Minsky,et al.  Steps toward Artificial Intelligence , 1995, Proceedings of the IRE.

[931]  R. FitzHugh Impulses and Physiological States in Theoretical Models of Nerve Membrane. , 1961, Biophysical journal.

[932]  Henry J. Kelley,et al.  Gradient Theory of Optimal Flight Paths , 1960 .

[933]  R. L. Stratonovich CONDITIONAL MARKOV PROCESSES , 1960 .

[934]  Arthur L. Samuel,et al.  Some Studies in Machine Learning Using the Game of Checkers , 1967, IBM J. Res. Dev..

[935]  D. Hubel,et al.  Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.

[936]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[937]  A. E. Vries,et al.  Separation of 14C16O and 12C18O by thermal diffusion , 1956 .

[938]  M. Hestenes,et al.  Methods of conjugate gradients for solving linear systems , 1952 .

[939]  David A. Huffman,et al.  A method for the construction of minimum-redundancy codes , 1952, Proceedings of the IRE.

[940]  D. Gabor,et al.  Theory of communication. Part 1: The analysis of information , 1946 .

[941]  N. Rashevsky The mathematical biophysics of some mental phenomena , 1945 .

[942]  Kenneth Levenberg A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[943]  A. Turing On Computable Numbers, with an Application to the Entscheidungsproblem. , 1937 .

[944]  A. Church An Unsolvable Problem of Elementary Number Theory , 1936 .

[945]  K. Gödel Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I , 1931 .

[946]  HighWire Press Philosophical Transactions of the Royal Society of London , 1781, The London Medical Journal.

[947]  K. F. Gauss,et al.  Theoria combinationis observationum erroribus minimis obnoxiae , 1823 .

[948]  IN -F , 2022 .

[949]  RussLL L. Ds Vnlos,et al.  SPATIAL FREQUENCY SELECTIVITY OF CELLS IN MACAQUE VISUAL CORTEX , 2022 .