Eigenvalue Decay Implies Polynomial-Time Learnability for Neural
Networks

Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks

Neural Information Processing Systems (NeurIPS), 2017

11 August 2017

Adam R. Klivans

ArXiv (abs)PDF HTML

Papers citing "Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks"

16 / 16 papers shown

Title
Perturbing the Derivative: Wild Refitting for Model-Free Evaluation of Machine Learning Models under Bregman Losses Haichen Hu David Simchi-Levi 429 0 0 02 Sep 2025
Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and GeneralizationInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2024 Yutong Wang Rishi Sonthalia Wei Hu 288 7 0 12 Mar 2024
Learning Graph Neural Networks with Approximate Gradient DescentAAAI Conference on Artificial Intelligence (AAAI), 2020 Qunwei Li Shaofeng Zou Leon Wenliang Zhong GNN 314 1 0 07 Dec 2020
Dissipative Deep Neural Dynamical Systems Ján Drgoňa Soumya Vasisht Aaron Tuor D. Vrabie 304 11 0 26 Nov 2020
From Boltzmann Machines to Neural Networks and Back AgainNeural Information Processing Systems (NeurIPS), 2020 Surbhi Goel Adam R. Klivans Frederic Koehler 183 7 0 25 Jul 2020
Frequency Bias in Neural Networks for Input of Non-Uniform DensityInternational Conference on Machine Learning (ICML), 2020 Ronen Basri Meirav Galun Amnon Geifman David Jacobs Yoni Kasten S. Kritchman 258 216 0 10 Mar 2020
Bayesian experimental design using regularized determinantal point processesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019 Michal Derezinski Feynman T. Liang Michael W. Mahoney 149 27 0 10 Jun 2019
On the Learnability of Deep Random Networks Abhimanyu Das Sreenivas Gollapudi Ravi Kumar Rina Panigrahy 115 8 0 08 Apr 2019
Optimal Sketching Bounds for Exp-concave Stochastic Minimization Naman Agarwal Alon Gonen 218 0 0 21 May 2018
How Many Samples are Needed to Estimate a Convolutional or Recurrent Neural Network? S. Du Yining Wang Xiyu Zhai Sivaraman Balakrishnan Ruslan Salakhutdinov Aarti Singh SSL 207 59 0 21 May 2018
Improved Learning of One-hidden-layer Convolutional Neural Networks with Overlaps S. Du Surbhi Goel MLT 180 17 0 20 May 2018
Learning One Convolutional Layer with Overlapping Patches Surbhi Goel Adam R. Klivans Raghu Meka MLT 126 81 0 07 Feb 2018
To understand deep learning we need to understand kernel learning M. Belkin Siyuan Ma Soumik Mandal 348 434 0 05 Feb 2018
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima S. Du Jason D. Lee Yuandong Tian Barnabás Póczós Aarti Singh MLT 368 241 0 03 Dec 2017
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time Surbhi Goel Adam R. Klivans 335 52 0 18 Sep 2017
Convergence Analysis of Two-layer Neural Networks with ReLU Activation Yuanzhi Li Yang Yuan MLT 336 674 0 28 May 2017