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[1] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[2] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[3] Gary R. Bradski,et al. Real time face and object tracking as a component of a perceptual user interface , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).
[4] Sepp Hochreiter,et al. The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[5] J. Hawkins,et al. On Intelligence , 2004 .
[6] Xiaogang Wang,et al. Visual Tracking with Fully Convolutional Networks , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[7] A D Wissner-Gross,et al. Causal entropic forces. , 2013, Physical review letters.
[8] A. Angelucci,et al. Contribution of feedforward, lateral and feedback connections to the classical receptive field center and extra-classical receptive field surround of primate V1 neurons. , 2006, Progress in brain research.
[9] Douglas M. Hawkins,et al. The Problem of Overfitting , 2004, J. Chem. Inf. Model..
[10] Terrence J. Sejnowski,et al. Slow Feature Analysis: Unsupervised Learning of Invariances , 2002, Neural Computation.
[11] J. Bullier,et al. Feedforward and feedback connections between areas V1 and V2 of the monkey have similar rapid conduction velocities. , 2001, Journal of neurophysiology.
[12] 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.
[13] A. W.,et al. Journal of chemical information and computer sciences. , 1995, Environmental science & technology.
[14] Peter Földiák,et al. Learning Invariance from Transformation Sequences , 1991, Neural Comput..
[15] C. Lee Giles,et al. Presenting and Analyzing the Results of AI Experiments: Data Averaging and Data Snooping , 1997, AAAI/IAAI.
[16] Yi Wu,et al. Online Object Tracking: A Benchmark , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Andreas Geiger,et al. Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..
[18] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[19] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[20] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Kiri L. Wagsta. Machine Learning that Matters , 2012 .
[22] Sergey Levine,et al. Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection , 2016, ISER.
[23] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[24] 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).
[25] Vibhav Vineet,et al. Struck: Structured Output Tracking with Kernels , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[26] Rodrigo Alvarez-Icaza,et al. Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations , 2014, Proceedings of the IEEE.
[27] R. Needham,et al. Artificial Intelligence : A General Survey , 2012 .
[28] Thomas Serre,et al. Object recognition with features inspired by visual cortex , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[29] Richard Socher,et al. Dynamic Memory Networks for Visual and Textual Question Answering , 2016, ICML.
[30] Yves Frégnac,et al. Adaptation of the simple or complex nature of V1 receptive fields to visual statistics , 2011, Nature Neuroscience.
[31] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[32] J. M. Hupé,et al. Cortical feedback improves discrimination between figure and background by V1, V2 and V3 neurons , 1998, Nature.
[33] Sven Behnke,et al. Learning Face Localization Using Hierarchical Recurrent Networks , 2002, ICANN.
[34] D. Hubel,et al. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.
[35] Alexei A. Efros,et al. Unbiased look at dataset bias , 2011, CVPR 2011.
[36] Antonio Torralba,et al. Anticipating Visual Representations from Unlabeled Video , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Alex Graves,et al. Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.
[38] Sergey Levine,et al. Unsupervised Learning for Physical Interaction through Video Prediction , 2016, NIPS.
[39] Marc'Aurelio Ranzato,et al. Video (language) modeling: a baseline for generative models of natural videos , 2014, ArXiv.
[40] Harris Drucker,et al. Comparison of learning algorithms for handwritten digit recognition , 1995 .
[41] Pedro M. Domingos. A few useful things to know about machine learning , 2012, Commun. ACM.
[42] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[43] Shai Avidan,et al. Real-time tracking-with-detection for coping with viewpoint change , 2015, Machine Vision and Applications.
[44] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[45] Gabriel Kreiman,et al. Unsupervised Learning of Visual Structure using Predictive Generative Networks , 2015, ArXiv.
[46] Sven Behnke,et al. Hierarchical Neural Networks for Image Interpretation , 2003, Lecture Notes in Computer Science.
[47] Charles Elkan,et al. Evaluating Classifiers , 2006 .
[48] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[49] Yi Li,et al. Robot Learning Manipulation Action Plans by "Watching" Unconstrained Videos from the World Wide Web , 2015, AAAI.
[50] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[51] Martial Hebert,et al. Cross-Stitch Networks for Multi-task Learning , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[52] Roman P. Pflugfelder,et al. Clustering of static-adaptive correspondences for deformable object tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[53] Murray Campbell,et al. Deep Blue , 2002, Artif. Intell..
[54] William B. Levy,et al. Temporal Sequence Compression by an Integrate-and-Fire Model of Hippocampal Area CA3 , 2004, Journal of Computational Neuroscience.
[55] Rasmus Berg Palm,et al. Prediction as a candidate for learning deep hierarchical models of data , 2012 .
[56] Jeffrey L. Elman,et al. Finding Structure in Time , 1990, Cogn. Sci..
[57] K. Lang,et al. Learning to tell two spirals apart , 1988 .
[58] Kiri Wagstaff,et al. Machine Learning that Matters , 2012, ICML.
[59] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[60] T. Poggio,et al. Hierarchical models of object recognition in cortex , 1999, Nature Neuroscience.
[61] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[62] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[63] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[64] R. Douglas,et al. Recurrent neuronal circuits in the neocortex , 2007, Current Biology.
[65] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[66] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[67] Olivier Sigaud,et al. Towards Deep Developmental Learning , 2016, IEEE Transactions on Cognitive and Developmental Systems.
[68] Samy Bengio,et al. Adversarial examples in the physical world , 2016, ICLR.
[69] Sven Behnke. Hebbian learning and competition in the neural abstraction pyramid , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).
[70] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[71] Martin A. Riedmiller,et al. A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.
[72] Steve B. Furber,et al. The SpiNNaker Project , 2014, Proceedings of the IEEE.
[73] Burn L. Lewis. In the game: The interface between Watson and Jeopardy! , 2012, IBM J. Res. Dev..
[74] Marc'Aurelio Ranzato,et al. Learning Longer Memory in Recurrent Neural Networks , 2014, ICLR.
[75] D. Hubel,et al. Receptive fields of single neurones in the cat's striate cortex , 1959, The Journal of physiology.
[76] S. Ebrahim,et al. Data dredging, bias, or confounding , 2002, BMJ : British Medical Journal.
[77] Johannes Schemmel,et al. Live demonstration: A scaled-down version of the BrainScaleS wafer-scale neuromorphic system , 2012, 2012 IEEE International Symposium on Circuits and Systems.
[78] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[79] Zdenek Kalal,et al. Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[80] Rajesh P. N. Rao,et al. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. , 1999 .
[81] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[82] Samy Bengio,et al. Torch: a modular machine learning software library , 2002 .
[83] R. Du,et al. What Happened at the DARPA Robotics Challenge , and Why ? , 2015 .
[84] William B. Levy,et al. Interpreting hippocampal function as recoding and forecasting , 2005, Neural Networks.
[85] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[86] D. Costarelli,et al. Constructive Approximation by Superposition of Sigmoidal Functions , 2013 .
[87] James G. King,et al. Reconstruction and Simulation of Neocortical Microcircuitry , 2015, Cell.
[88] A. Clark. Whatever next? Predictive brains, situated agents, and the future of cognitive science. , 2013, The Behavioral and brain sciences.
[89] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..
[90] G. Amdhal,et al. Validity of the single processor approach to achieving large scale computing capabilities , 1967, AFIPS '67 (Spring).
[91] Andrew S. Cassidy,et al. Real-Time Scalable Cortical Computing at 46 Giga-Synaptic OPS/Watt with ~100× Speedup in Time-to-Solution and ~100,000× Reduction in Energy-to-Solution , 2014, SC14: International Conference for High Performance Computing, Networking, Storage and Analysis.
[92] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[93] PAUL J. WERBOS,et al. Generalization of backpropagation with application to a recurrent gas market model , 1988, Neural Networks.
[94] Razvan Pascanu,et al. Theano: A CPU and GPU Math Compiler in Python , 2010, SciPy.
[95] Michael J. Swain,et al. Indexing via color histograms , 1990, [1990] Proceedings Third International Conference on Computer Vision.
[96] Alexei A. Efros,et al. Unsupervised Visual Representation Learning by Context Prediction , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[97] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[98] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[99] Seunghoon Hong,et al. Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network , 2015, ICML.
[100] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[101] Sergey Levine,et al. Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..