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Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced

4 June 2018

Papers citing "Algorithmic Regularization in Learning Deep Homogeneous Models: Layers are Automatically Balanced"

23 / 123 papers shown

Title
The Landscape of Matrix Factorization Revisited Hossein Valavi Sulin Liu Peter J. Ramadge 138 5 0 27 Feb 2020
Revealing the Structure of Deep Neural Networks via Convex DualityInternational Conference on Machine Learning (ICML), 2020 Tolga Ergen Mert Pilanci MLT 362 74 0 22 Feb 2020
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective FunctionNeural Information Processing Systems (NeurIPS), 2020 Lingkai Kong Molei Tao 99 30 0 14 Feb 2020
Lookahead: A Far-Sighted Alternative of Magnitude-based PruningInternational Conference on Learning Representations (ICLR), 2020 Sejun Park Jaeho Lee Sangwoo Mo Jinwoo Shin 104 100 0 12 Feb 2020
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic LossAnnual Conference Computational Learning Theory (COLT), 2020 Lénaïc Chizat Francis R. Bach MLT 566 363 0 11 Feb 2020
On the Principle of Least Symmetry Breaking in Shallow ReLU Models Yossi Arjevani M. Field 230 8 0 26 Dec 2019
On the Anomalous Generalization of GANs Jinchen Xuan Yunchang Yang Ze Yang Di He Liwei Wang 118 3 0 27 Sep 2019
Theoretical Issues in Deep Networks: Approximation, Optimization and GeneralizationProceedings of the National Academy of Sciences of the United States of America (PNAS), 2019 T. Poggio Andrzej Banburski Q. Liao ODL 164 183 0 25 Aug 2019
Gradient Descent Maximizes the Margin of Homogeneous Neural NetworksInternational Conference on Learning Representations (ICLR), 2019 Kaifeng Lyu Jian Li 434 363 0 13 Jun 2019
Implicit Regularization in Deep Matrix FactorizationNeural Information Processing Systems (NeurIPS), 2019 Sanjeev Arora Nadav Cohen Wei Hu Yuping Luo AI4CE 352 556 0 31 May 2019
On Stationary-Point Hitting Time and Ergodicity of Stochastic Gradient Langevin DynamicsJournal of machine learning research (JMLR), 2019 Xi Chen S. Du Xin T. Tong 378 33 0 30 Apr 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 593 982 0 26 Apr 2019
Theory III: Dynamics and Generalization in Deep Networks Andrzej Banburski Q. Liao Alycia Lee Lorenzo Rosasco Fernanda De La Torre Jack Hidary T. Poggio AI4CE 234 3 0 12 Mar 2019
Width Provably Matters in Optimization for Deep Linear Neural Networks S. Du Wei Hu 286 101 0 24 Jan 2019
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits Xialiang Dou Tengyuan Liang MLT 228 42 0 21 Jan 2019
A Geometric Approach of Gradient Descent Algorithms in Linear Neural Networks S. Mahabadi Zhenyu Liao Romain Couillet 153 16 0 08 Nov 2018
A Convergence Analysis of Gradient Descent for Deep Linear Neural Networks Sanjeev Arora Nadav Cohen Noah Golowich Wei Hu 439 325 0 04 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 667 1,335 0 04 Oct 2018
Gradient descent aligns the layers of deep linear networks Ziwei Ji Matus Telgarsky 190 274 0 04 Oct 2018
Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks Ohad Shamir 181 48 0 23 Sep 2018
NETT: Solving Inverse Problems with Deep Neural NetworksInverse Problems (IP), 2018 Housen Li Johannes Schwab Stephan Antholzer Markus Haltmeier 254 263 0 28 Feb 2018
$$\mathcal{G}$-SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space$ $\mathcal{G}$ -SGD: Optimizing ReLU Neural Networks in its Positively Scale-Invariant Space Qi Meng Shuxin Zheng Huishuai Zhang Wei Chen Zhi-Ming Ma Tie-Yan Liu 394 42 0 11 Feb 2018
High-dimensional dynamics of generalization error in neural networks Madhu S. Advani Andrew M. Saxe AI4CE 253 500 0 10 Oct 2017