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Deep Networks and the Multiple Manifold Problem

25 August 2020

Papers citing "Deep Networks and the Multiple Manifold Problem"

40 / 40 papers shown

Title
The Interpolation Phase Transition in Neural Networks: Memorization and Generalization under Lazy Training Andrea Montanari Yiqiao Zhong 180 97 0 25 Jul 2020
Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics Taiji Suzuki 56 21 0 11 Jul 2020
When Do Neural Networks Outperform Kernel Methods? Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari 117 189 0 24 Jun 2020
Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent Surbhi Goel Aravind Gollakota Zhihan Jin Sushrut Karmalkar Adam R. Klivans MLT ODL 71 72 0 22 Jun 2020
Directional convergence and alignment in deep learning Ziwei Ji Matus Telgarsky 68 171 0 11 Jun 2020
Deep Learning Techniques for Inverse Problems in Imaging Greg Ongie A. Jalal Christopher A. Metzler Richard G. Baraniuk A. Dimakis Rebecca Willett 101 535 0 12 May 2020
Mehler's Formula, Branching Process, and Compositional Kernels of Deep Neural Networks Tengyuan Liang Hai Tran-Bach 46 11 0 09 Apr 2020
The large learning rate phase of deep learning: the catapult mechanism Aitor Lewkowycz Yasaman Bahri Ethan Dyer Jascha Narain Sohl-Dickstein Guy Gur-Ari ODL 210 241 0 04 Mar 2020
The Two Regimes of Deep Network Training Guillaume Leclerc Aleksander Madry 92 45 0 24 Feb 2020
Generalisation error in learning with random features and the hidden manifold model Federica Gerace Bruno Loureiro Florent Krzakala M. Mézard Lenka Zdeborová 80 172 0 21 Feb 2020
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss Lénaïc Chizat Francis R. Bach MLT 170 341 0 11 Feb 2020
Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width Yu Bai Ben Krause Huan Wang Caiming Xiong R. Socher 66 22 0 10 Feb 2020
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks Yu Bai Jason D. Lee 65 116 0 03 Oct 2019
Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks Ziwei Ji Matus Telgarsky 98 178 0 26 Sep 2019
Modelling the influence of data structure on learning in neural networks: the hidden manifold model Sebastian Goldt M. Mézard Florent Krzakala Lenka Zdeborová BDL 78 51 0 25 Sep 2019
Asymptotics of Wide Networks from Feynman Diagrams Ethan Dyer Guy Gur-Ari 100 115 0 25 Sep 2019
Dynamics of Deep Neural Networks and Neural Tangent Hierarchy Jiaoyang Huang H. Yau 62 151 0 18 Sep 2019
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks Yuanzhi Li Colin Wei Tengyu Ma 82 299 0 10 Jul 2019
Robust and interpretable blind image denoising via bias-free convolutional neural networks S. Mohan Zahra Kadkhodaie Eero P. Simoncelli C. Fernandez‐Granda AI4CE 90 125 0 13 Jun 2019
Generalization Guarantees for Neural Networks via Harnessing the Low-rank Structure of the Jacobian Samet Oymak Zalan Fabian Mingchen Li Mahdi Soltanolkotabi MLT 85 88 0 12 Jun 2019
Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks Yuan Cao Quanquan Gu MLT AI4CE 107 392 0 30 May 2019
What Can ResNet Learn Efficiently, Going Beyond Kernels? Zeyuan Allen-Zhu Yuanzhi Li 416 183 0 24 May 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 93 243 0 27 Apr 2019
On Exact Computation with an Infinitely Wide Neural Net Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruslan Salakhutdinov Ruosong Wang 249 928 0 26 Apr 2019
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee Lechao Xiao S. Schoenholz Yasaman Bahri Roman Novak Jascha Narain Sohl-Dickstein Jeffrey Pennington 218 1,111 0 18 Feb 2019
Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit Song Mei Theodor Misiakiewicz Andrea Montanari MLT 90 279 0 16 Feb 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 230 974 0 24 Jan 2019
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang MLT 217 775 0 12 Nov 2018
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 301 1,470 0 09 Nov 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 248 1,276 0 04 Oct 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 319 3,225 0 20 Jun 2018
Rate-Optimal Denoising with Deep Neural Networks Reinhard Heckel Wen Huang Paul Hand V. Voroninski 64 23 0 22 May 2018
A Mean Field View of the Landscape of Two-Layers Neural Networks Song Mei Andrea Montanari Phan-Minh Nguyen MLT 111 863 0 18 Apr 2018
Deep Image Prior Dmitry Ulyanov Andrea Vedaldi Victor Lempitsky SupR 163 3,169 0 29 Nov 2017
Sharp asymptotic and finite-sample rates of convergence of empirical measures in Wasserstein distance Jonathan Niles-Weed Francis R. Bach 216 421 0 01 Jul 2017
Compressed Sensing using Generative Models Ashish Bora A. Jalal Eric Price A. Dimakis 182 813 0 09 Mar 2017
Group Equivariant Convolutional Networks Taco S. Cohen Max Welling BDL 184 1,947 0 24 Feb 2016
Concentration inequalities for order statistics S. Boucheron Maud Thomas 164 82 0 31 Jul 2012
Invariant Scattering Convolution Networks Joan Bruna S. Mallat 138 1,279 0 05 Mar 2012
Group Invariant Scattering S. Mallat 135 993 0 12 Jan 2011