Unreasonable Effectiveness of Learning Neural Networks: From Accessible
States and Robust Ensembles to Basic Algorithmic Schemes

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Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes

20 May 2016

Alessandro Ingrosso

Carlo Lucibello

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Papers citing "Unreasonable Effectiveness of Learning Neural Networks: From Accessible States and Robust Ensembles to Basic Algorithmic Schemes"

18 / 68 papers shown

Title
Comparing Dynamics: Deep Neural Networks versus Glassy Systems Marco Baity-Jesi Levent Sagun Mario Geiger S. Spigler Gerard Ben Arous C. Cammarota Yann LeCun Matthieu Wyart Giulio Biroli AI4CE 174 117 0 19 Mar 2018
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning Yao Zhang Andrew M. Saxe Madhu S. Advani A. Lee 111 60 0 05 Mar 2018
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks Qianxiao Li Shuji Hao 138 77 0 04 Mar 2018
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors Gintare Karolina Dziugaite Daniel M. Roy MLT 147 145 0 26 Dec 2017
A trans-disciplinary review of deep learning research for water resources scientists Chaopeng Shen AI4CE 299 725 0 06 Dec 2017
Stochastic gradient descent performs variational inference, converges to limit cycles for deep networks Pratik Chaudhari Stefano Soatto MLT 164 309 0 30 Oct 2017
On the role of synaptic stochasticity in training low-precision neural networks Carlo Baldassi Federica Gerace H. Kappen Carlo Lucibello Luca Saglietti Enzo Tartaglione R. Zecchina 119 23 0 26 Oct 2017
Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models Jean Barbier Florent Krzakala N. Macris Léo Miolane Lenka Zdeborová 182 278 0 10 Aug 2017
Parle: parallelizing stochastic gradient descent Pratik Chaudhari Carlo Baldassi R. Zecchina Stefano Soatto Ameet Talwalkar Adam M. Oberman ODL FedML 111 23 0 03 Jul 2017
Towards Understanding Generalization of Deep Learning: Perspective of Loss Landscapes Lei Wu Zhanxing Zhu E. Weinan ODL 133 222 0 30 Jun 2017
Efficiency of quantum versus classical annealing in non-convex learning problems Carlo Baldassi R. Zecchina 144 48 0 26 Jun 2017
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks Levent Sagun Utku Evci V. U. Güney Yann N. Dauphin Léon Bottou 191 425 0 14 Jun 2017
A General Theory for Training Learning Machine Hong Zhao AI4CE 28 3 0 23 Apr 2017
Deep Relaxation: partial differential equations for optimizing deep neural networks Pratik Chaudhari Adam M. Oberman Stanley Osher Stefano Soatto G. Carlier 200 155 0 17 Apr 2017
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data Gintare Karolina Dziugaite Daniel M. Roy 267 846 0 31 Mar 2017
Reinforced stochastic gradient descent for deep neural network learning Haiping Huang Taro Toyoizumi ODL 73 1 0 27 Jan 2017
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys Pratik Chaudhari A. Choromańska Stefano Soatto Yann LeCun Carlo Baldassi C. Borgs J. Chayes Levent Sagun R. Zecchina ODL 297 802 0 06 Nov 2016
On the energy landscape of deep networks Pratik Chaudhari Stefano Soatto ODL 191 27 0 20 Nov 2015