A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models

14 January 2024

Papers citing "A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models"

11 / 11 papers shown

Title
Deep learning with missing data Tianyi Ma Tengyao Wang R. Samworth 59 0 0 21 Apr 2025
An Analysis Framework for Understanding Deep Neural Networks Based on Network Dynamics Yuchen Lin Yong Zhang Sihan Feng Hong Zhao 36 0 0 05 Jan 2025
iKAN: Global Incremental Learning with KAN for Human Activity Recognition Across Heterogeneous Datasets Mengxi Liu Sizhen Bian Bo Zhou P. Lukowicz HAI CLL 38 19 0 03 Jun 2024
Kolmogorov-Arnold Network for Satellite Image Classification in Remote Sensing Minjong Cheon 43 43 0 02 Jun 2024
Diffusion Models are Minimax Optimal Distribution Estimators Kazusato Oko Shunta Akiyama Taiji Suzuki DiffM 72 85 0 03 Mar 2023
STaSy: Score-based Tabular data Synthesis Jayoung Kim C. Lee Noseong Park DiffM 56 59 0 08 Oct 2022
Convergence of score-based generative modeling for general data distributions Holden Lee Jianfeng Lu Yixin Tan DiffM 191 128 0 26 Sep 2022
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 135 246 0 22 Sep 2022
Diffusion Models: A Comprehensive Survey of Methods and Applications Ling Yang Zhilong Zhang Yingxia Shao Shenda Hong Runsheng Xu Yue Zhao Wentao Zhang Bin Cui Ming-Hsuan Yang DiffM MedIm 224 1,302 0 02 Sep 2022
A new similarity measure for covariate shift with applications to nonparametric regression Reese Pathak Cong Ma Martin J. Wainwright 63 31 0 06 Feb 2022
Statistical guarantees for generative models without domination Nicolas Schreuder Victor-Emmanuel Brunel A. Dalalyan GAN 62 34 0 19 Oct 2020