Local minima in training of neural networks
暂无分享,去创建一个
Razvan Pascanu | Wojciech Marian Czarnecki | Grzegorz Swirszcz | Wojciech M. Czarnecki | Razvan Pascanu | G. Swirszcz
[1] E. Wigner. On the Distribution of the Roots of Certain Symmetric Matrices , 1958 .
[2] Kurt Hornik,et al. Neural networks and principal component analysis: Learning from examples without local minima , 1989, Neural Networks.
[3] Yan V Fyodorov,et al. Replica Symmetry Breaking Condition Exposed by Random Matrix Calculation of Landscape Complexity , 2007, cond-mat/0702601.
[4] A. Bray,et al. Statistics of critical points of Gaussian fields on large-dimensional spaces. , 2006, Physical review letters.
[5] James L. McClelland,et al. Learning hierarchical category structure in deep neural networks , 2013 .
[6] Geoffrey E. Hinton,et al. On the importance of initialization and momentum in deep learning , 2013, ICML.
[7] Surya Ganguli,et al. Identifying and attacking the saddle point problem in high-dimensional non-convex optimization , 2014, NIPS.
[8] Surya Ganguli,et al. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks , 2013, ICLR.
[9] Quoc V. Le,et al. Sequence to Sequence Learning with Neural Networks , 2014, NIPS.
[10] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[11] 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).
[12] Oriol Vinyals,et al. Qualitatively characterizing neural network optimization problems , 2014, ICLR.
[13] Yann LeCun,et al. Explorations on high dimensional landscapes , 2014, ICLR.
[14] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[15] Yann LeCun,et al. The Loss Surfaces of Multilayer Networks , 2014, AISTATS.
[16] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[17] Jian Sun,et al. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[18] Yoshua Bengio,et al. Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.
[19] Kenji Kawaguchi,et al. Deep Learning without Poor Local Minima , 2016, NIPS.
[20] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Ohad Shamir,et al. On the Quality of the Initial Basin in Overspecified Neural Networks , 2015, ICML.
[22] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[23] Seyed-Mohsen Moosavi-Dezfooli,et al. Robustness of classifiers: from adversarial to random noise , 2016, NIPS.
[24] Alex Graves,et al. Asynchronous Methods for Deep Reinforcement Learning , 2016, ICML.
[25] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[26] Daniel Soudry,et al. No bad local minima: Data independent training error guarantees for multilayer neural networks , 2016, ArXiv.
[27] Max Tegmark,et al. Why Does Deep and Cheap Learning Work So Well? , 2016, Journal of Statistical Physics.
[28] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[29] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[30] Ohad Shamir,et al. Distribution-Specific Hardness of Learning Neural Networks , 2016, J. Mach. Learn. Res..