On the Complexity of Learning Neural Networks

On the Complexity of Learning Neural Networks

14 July 2017

Santosh Vempala

Papers citing "On the Complexity of Learning Neural Networks"

12 / 12 papers shown

Title
Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy Amit Daniely Nathan Srebro Gal Vardi 33 4 0 15 Feb 2023
Learning ReLU networks to high uniform accuracy is intractable Julius Berner Philipp Grohs F. Voigtlaender 32 4 0 26 May 2022
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks Sitan Chen Aravind Gollakota Adam R. Klivans Raghu Meka 27 30 0 10 Feb 2022
From Local Pseudorandom Generators to Hardness of Learning Amit Daniely Gal Vardi 109 30 0 20 Jan 2021
Learning Graph Neural Networks with Approximate Gradient Descent Qunwei Li Shaofeng Zou Leon Wenliang Zhong GNN 32 1 0 07 Dec 2020
Learning Deep ReLU Networks Is Fixed-Parameter Tractable Sitan Chen Adam R. Klivans Raghu Meka 22 36 0 28 Sep 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 24 70 0 22 Jun 2020
On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond Xingguo Li Junwei Lu Zhaoran Wang Jarvis Haupt T. Zhao 27 78 0 13 Jun 2018
Adversarial examples from computational constraints Sébastien Bubeck Eric Price Ilya P. Razenshteyn AAML 65 230 0 25 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 21 57 0 21 May 2018
Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds Santosh Vempala John Wilmes MLT 16 50 0 07 May 2018
Benefits of depth in neural networks Matus Telgarsky 153 603 0 14 Feb 2016