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

20 May 2016

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

24 / 24 papers shown

Title
Entropy-Guided Sampling of Flat Modes in Discrete Spaces Pinaki Mohanty Riddhiman Bhattacharya Ruqi Zhang 218 0 0 05 May 2025
High-dimensional manifold of solutions in neural networks: insights from statistical physics Enrico M. Malatesta 56 4 0 20 Feb 2025
Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-dimensional Tokens Vittorio Erba Emanuele Troiani Luca Biggio Antoine Maillard Lenka Zdeborová 28 0 0 24 Oct 2024
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss T. Getu Georges Kaddoum M. Bennis 40 1 0 13 Sep 2023
Quantifying Relevance in Learning and Inference M. Marsili Y. Roudi 14 18 0 01 Feb 2022
Binary perceptron: efficient algorithms can find solutions in a rare well-connected cluster Emmanuel Abbe Shuangping Li Allan Sly MQ 28 30 0 04 Nov 2021
Learning through atypical "phase transitions" in overparameterized neural networks Carlo Baldassi Clarissa Lauditi Enrico M. Malatesta R. Pacelli Gabriele Perugini R. Zecchina 33 26 0 01 Oct 2021
Entropic alternatives to initialization Daniele Musso 37 1 0 16 Jul 2021
Unveiling the structure of wide flat minima in neural networks Carlo Baldassi Clarissa Lauditi Enrico M. Malatesta Gabriele Perugini R. Zecchina 21 32 0 02 Jul 2021
Some Remarks on Replicated Simulated Annealing Vicent Gripon Matthias Löwe Franck Vermet 19 2 0 30 Sep 2020
Entropic gradient descent algorithms and wide flat minima Fabrizio Pittorino C. Lucibello Christoph Feinauer Gabriele Perugini Carlo Baldassi Elizaveta Demyanenko R. Zecchina ODL MLT 35 33 0 14 Jun 2020
How to iron out rough landscapes and get optimal performances: Averaged Gradient Descent and its application to tensor PCA Giulio Biroli C. Cammarota F. Ricci-Tersenghi 39 27 0 29 May 2019
Shaping the learning landscape in neural networks around wide flat minima Carlo Baldassi Fabrizio Pittorino R. Zecchina MLT 26 82 0 20 May 2019
Implicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning Charles H. Martin Michael W. Mahoney AI4CE 47 192 0 02 Oct 2018
Optimization of neural networks via finite-value quantum fluctuations Masayuki Ohzeki Shuntaro Okada Masayoshi Terabe S. Taguchi 21 21 0 01 Jul 2018
Non-Vacuous Generalization Bounds at the ImageNet Scale: A PAC-Bayesian Compression Approach Wenda Zhou Victor Veitch Morgane Austern Ryan P. Adams Peter Orbanz 44 211 0 16 Apr 2018
Comparing Dynamics: Deep Neural Networks versus Glassy Systems Marco Baity-Jesi Levent Sagun Mario Geiger S. Spigler Gerard Ben Arous C. Cammarota Yann LeCun M. Wyart Giulio Biroli AI4CE 42 113 0 19 Mar 2018
An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks Qianxiao Li Shuji Hao 27 75 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 30 144 0 26 Dec 2017
A trans-disciplinary review of deep learning research for water resources scientists Chaopeng Shen AI4CE 33 682 0 06 Dec 2017
Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models Jean Barbier Florent Krzakala N. Macris Léo Miolane Lenka Zdeborová 37 260 0 10 Aug 2017
Deep Relaxation: partial differential equations for optimizing deep neural networks Pratik Chaudhari Adam M. Oberman Stanley Osher Stefano Soatto G. Carlier 27 153 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 50 802 0 31 Mar 2017
On the energy landscape of deep networks Pratik Chaudhari Stefano Soatto ODL 43 27 0 20 Nov 2015