暂无分享,去创建一个
[1] Phillip D. Summers,et al. A Methodology for LISP Program Construction from Examples , 1977, J. ACM.
[2] Alan W. Biermann,et al. The Inference of Regular LISP Programs from Examples , 1978, IEEE Transactions on Systems, Man, and Cybernetics.
[3] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[4] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[5] David G. Lowe,et al. Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.
[6] Yoshua Bengio,et al. A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..
[7] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[8] Jürgen Schmidhuber,et al. Modeling systems with internal state using evolino , 2005, GECCO '05.
[9] 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).
[10] Geoffrey E. Hinton,et al. Three new graphical models for statistical language modelling , 2007, ICML '07.
[11] G. Evans,et al. Learning to Optimize , 2008 .
[12] Dario Floreano,et al. Neuroevolution: from architectures to learning , 2008, Evol. Intell..
[13] Yann LeCun,et al. What is the best multi-stage architecture for object recognition? , 2009, 2009 IEEE 12th International Conference on Computer Vision.
[14] Kenneth O. Stanley,et al. A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks , 2009, Artificial Life.
[15] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[16] Michael I. Jordan,et al. Learning Programs: A Hierarchical Bayesian Approach , 2010, ICML.
[17] Yoshua Bengio,et al. Algorithms for Hyper-Parameter Optimization , 2011, NIPS.
[18] Yoshua Bengio,et al. Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..
[19] Jasper Snoek,et al. Practical Bayesian Optimization of Machine Learning Algorithms , 2012, NIPS.
[20] Marc'Aurelio Ranzato,et al. Large Scale Distributed Deep Networks , 2012, NIPS.
[21] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[22] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[23] Geoffrey Zweig,et al. Context dependent recurrent neural network language model , 2012, 2012 IEEE Spoken Language Technology Workshop (SLT).
[24] David D. Cox,et al. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures , 2013, ICML.
[25] Geoffrey E. Hinton,et al. On the importance of initialization and momentum in deep learning , 2013, ICML.
[26] Wei-Chen Cheng,et al. Language modeling with sum-product networks , 2014, INTERSPEECH.
[27] Razvan Pascanu,et al. How to Construct Deep Recurrent Neural Networks , 2013, ICLR.
[28] Qiang Chen,et al. Network In Network , 2013, ICLR.
[29] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[30] Wojciech Zaremba,et al. Recurrent Neural Network Regularization , 2014, ArXiv.
[31] Thomas Brox,et al. Striving for Simplicity: The All Convolutional Net , 2014, ICLR.
[32] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[33] Wojciech Zaremba,et al. An Empirical Exploration of Recurrent Network Architectures , 2015, ICML.
[34] Joshua B. Tenenbaum,et al. Human-level concept learning through probabilistic program induction , 2015, Science.
[35] Prabhat,et al. Scalable Bayesian Optimization Using Deep Neural Networks , 2015, ICML.
[36] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[37] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[39] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[40] Aaron Klein,et al. Towards Automatically-Tuned Neural Networks , 2016, AutoML@ICML.
[41] Zoubin Ghahramani,et al. A Theoretically Grounded Application of Dropout in Recurrent Neural Networks , 2015, NIPS.
[42] Jakob Verbeek,et al. Convolutional Neural Fabrics , 2016, NIPS.
[43] Nikos Komodakis,et al. Wide Residual Networks , 2016, BMVC.
[44] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Marc'Aurelio Ranzato,et al. Sequence Level Training with Recurrent Neural Networks , 2015, ICLR.
[46] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[47] Kilian Q. Weinberger,et al. Deep Networks with Stochastic Depth , 2016, ECCV.
[48] Dan Klein,et al. Learning to Compose Neural Networks for Question Answering , 2016, NAACL.
[49] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[50] Alexander M. Rush,et al. Character-Aware Neural Language Models , 2015, AAAI.
[51] Xinyun Chen. Under Review as a Conference Paper at Iclr 2017 Delving into Transferable Adversarial Ex- Amples and Black-box Attacks , 2016 .
[52] Yang Liu,et al. Minimum Risk Training for Neural Machine Translation , 2015, ACL.
[53] Nando de Freitas,et al. Neural Programmer-Interpreters , 2015, ICLR.
[54] Quoc V. Le,et al. Neural Programmer: Inducing Latent Programs with Gradient Descent , 2015, ICLR.
[55] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[56] Hakan Inan,et al. Tying Word Vectors and Word Classifiers: A Loss Framework for Language Modeling , 2016, ICLR.
[57] C A Nelson,et al. Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.
[58] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[59] Lior Wolf,et al. Using the Output Embedding to Improve Language Models , 2016, EACL.
[60] Jürgen Schmidhuber,et al. Recurrent Highway Networks , 2016, ICML.
[61] Gregory Shakhnarovich,et al. FractalNet: Ultra-Deep Neural Networks without Residuals , 2016, ICLR.
[62] Richard Socher,et al. Pointer Sentinel Mixture Models , 2016, ICLR.