On Lazy Training in Differentiable Programming

19 December 2018

Papers citing "On Lazy Training in Differentiable Programming"

27 / 227 papers shown

Title
A Mean-field Analysis of Deep ResNet and Beyond: Towards Provable Optimization Via Overparameterization From Depth Yiping Lu Chao Ma Yulong Lu Jianfeng Lu Lexing Ying MLT 39 78 0 11 Mar 2020
Frequency Bias in Neural Networks for Input of Non-Uniform Density Ronen Basri Meirav Galun Amnon Geifman David Jacobs Yoni Kasten S. Kritchman 39 183 0 10 Mar 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 159 234 0 04 Mar 2020
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime Stéphane dÁscoli Maria Refinetti Giulio Biroli Florent Krzakala 93 152 0 02 Mar 2020
Loss landscapes and optimization in over-parameterized non-linear systems and neural networks Chaoyue Liu Libin Zhu M. Belkin ODL 14 247 0 29 Feb 2020
A Spectral Analysis of Dot-product Kernels M. Scetbon Zaïd Harchaoui 159 2 0 28 Feb 2020
Convex Geometry and Duality of Over-parameterized Neural Networks Tolga Ergen Mert Pilanci MLT 42 54 0 25 Feb 2020
Neural Networks are Convex Regularizers: Exact Polynomial-time Convex Optimization Formulations for Two-layer Networks Mert Pilanci Tolga Ergen 26 116 0 24 Feb 2020
An Optimization and Generalization Analysis for Max-Pooling Networks Alon Brutzkus Amir Globerson MLT AI4CE 16 4 0 22 Feb 2020
Generalisation error in learning with random features and the hidden manifold model Federica Gerace Bruno Loureiro Florent Krzakala M. Mézard Lenka Zdeborová 25 165 0 21 Feb 2020
Self-Distillation Amplifies Regularization in Hilbert Space H. Mobahi Mehrdad Farajtabar Peter L. Bartlett 19 226 0 13 Feb 2020
Implicit Bias of Gradient Descent for Wide Two-layer Neural Networks Trained with the Logistic Loss Lénaïc Chizat Francis R. Bach MLT 39 327 0 11 Feb 2020
On the infinite width limit of neural networks with a standard parameterization Jascha Narain Sohl-Dickstein Roman Novak S. Schoenholz Jaehoon Lee 32 47 0 21 Jan 2020
Optimization for deep learning: theory and algorithms Ruoyu Sun ODL 25 168 0 19 Dec 2019
Neural Tangents: Fast and Easy Infinite Neural Networks in Python Roman Novak Lechao Xiao Jiri Hron Jaehoon Lee Alexander A. Alemi Jascha Narain Sohl-Dickstein S. Schoenholz 29 224 0 05 Dec 2019
Towards Understanding the Spectral Bias of Deep Learning Yuan Cao Zhiying Fang Yue Wu Ding-Xuan Zhou Quanquan Gu 35 214 0 03 Dec 2019
Non-Gaussian processes and neural networks at finite widths Sho Yaida 18 88 0 30 Sep 2019
Asymptotics of Wide Networks from Feynman Diagrams Ethan Dyer Guy Gur-Ari 29 113 0 25 Sep 2019
Finite Depth and Width Corrections to the Neural Tangent Kernel Boris Hanin Mihai Nica MDE 27 150 0 13 Sep 2019
The generalization error of random features regression: Precise asymptotics and double descent curve Song Mei Andrea Montanari 60 626 0 14 Aug 2019
Sparse Optimization on Measures with Over-parameterized Gradient Descent Lénaïc Chizat 21 92 0 24 Jul 2019
Neural Proximal/Trust Region Policy Optimization Attains Globally Optimal Policy Boyi Liu Qi Cai Zhuoran Yang Zhaoran Wang 24 108 0 25 Jun 2019
Kernel and Rich Regimes in Overparametrized Models Blake E. Woodworth Suriya Gunasekar Pedro H. P. Savarese E. Moroshko Itay Golan J. Lee Daniel Soudry Nathan Srebro 30 352 0 13 Jun 2019
A type of generalization error induced by initialization in deep neural networks Yaoyu Zhang Zhi-Qin John Xu Tao Luo Zheng Ma 9 49 0 19 May 2019
Linearized two-layers neural networks in high dimension Behrooz Ghorbani Song Mei Theodor Misiakiewicz Andrea Montanari MLT 18 241 0 27 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 34 1,076 0 18 Feb 2019
High-dimensional dynamics of generalization error in neural networks Madhu S. Advani Andrew M. Saxe AI4CE 78 464 0 10 Oct 2017