Hardness of Learning Neural Networks under the Manifold Hypothesis

3 June 2024

Papers citing "Hardness of Learning Neural Networks under the Manifold Hypothesis"

8 / 8 papers shown

Title
SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics Emmanuel Abbe Enric Boix-Adserà Theodor Misiakiewicz FedML MLT 79 73 0 21 Feb 2023
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions Sitan Chen Sinho Chewi Jungshian Li Yuanzhi Li Adil Salim Anru R. Zhang DiffM 135 247 0 22 Sep 2022
The Intrinsic Dimension of Images and Its Impact on Learning Phillip E. Pope Chen Zhu Ahmed Abdelkader Micah Goldblum Tom Goldstein 197 260 0 18 Apr 2021
From Local Pseudorandom Generators to Hardness of Learning Amit Daniely Gal Vardi 109 30 0 20 Jan 2021
Hyperbolic Deep Neural Networks: A Survey Wei Peng Tuomas Varanka Abdelrahman Mostafa Henglin Shi Guoying Zhao AI4CE 45 133 0 12 Jan 2021
Deep Networks and the Multiple Manifold Problem Sam Buchanan D. Gilboa John N. Wright 166 39 0 25 Aug 2020
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 259 3,239 0 24 Nov 2016
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 321 75,834 0 18 May 2015