Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space

28 October 2019

Papers citing "Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space"

12 / 12 papers shown

Title
Posterior and variational inference for deep neural networks with heavy-tailed weights Ismael Castillo Paul Egels BDL 60 4 0 05 Jun 2024
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks Fanghui Liu L. Dadi V. Cevher 85 2 0 29 Apr 2024
Neural Network Approximation for Pessimistic Offline Reinforcement Learning Di Wu Yuling Jiao Li Shen Haizhao Yang Xiliang Lu OffRL 34 1 0 19 Dec 2023
Analysis of the expected $L_2$ error of an over-parametrized deep neural network estimate learned by gradient descent without regularization Selina Drews Michael Kohler 38 3 0 24 Nov 2023
Exploring the Approximation Capabilities of Multiplicative Neural Networks for Smooth Functions Ido Ben-Shaul Tomer Galanti S. Dekel 33 3 0 11 Jan 2023
Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis Josip Jukić Jan Snajder 16 5 0 20 Dec 2022
Analysis of the rate of convergence of an over-parametrized deep neural network estimate learned by gradient descent Michael Kohler A. Krzyżak 32 10 0 04 Oct 2022
On the universal consistency of an over-parametrized deep neural network estimate learned by gradient descent Selina Drews Michael Kohler 30 14 0 30 Aug 2022
On the inability of Gaussian process regression to optimally learn compositional functions M. Giordano Kolyan Ray Johannes Schmidt-Hieber 55 12 0 16 May 2022
Estimating a regression function in exponential families by model selection Juntong Chen 32 2 0 13 Mar 2022
Drift estimation for a multi-dimensional diffusion process using deep neural networks Akihiro Oga Yuta Koike DiffM 21 5 0 26 Dec 2021
Estimation of a regression function on a manifold by fully connected deep neural networks Michael Kohler S. Langer U. Reif 22 4 0 20 Jul 2021