Wide and Deep Neural Networks Achieve Optimality for Classification

29 April 2022

Papers citing "Wide and Deep Neural Networks Achieve Optimality for Classification"

10 / 10 papers shown

Title
Deep Minimax Classifiers for Imbalanced Datasets with a Small Number of Minority Samples Hansung Choi Daewon Seo 46 0 0 24 Feb 2025
Overfitting Regimes of Nadaraya-Watson Interpolators Daniel Barzilai Guy Kornowski Ohad Shamir 76 0 0 11 Feb 2025
Generalization bounds for regression and classification on adaptive covering input domains Wen-Liang Hwang 29 0 0 29 Jul 2024
Classifying Overlapping Gaussian Mixtures in High Dimensions: From Optimal Classifiers to Neural Nets Khen Cohen Noam Levi Yaron Oz BDL 31 1 0 28 May 2024
A prediction rigidity formalism for low-cost uncertainties in trained neural networks Filippo Bigi Sanggyu Chong Michele Ceriotti Federico Grasselli 46 5 0 04 Mar 2024
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes Hyunouk Ko Xiaoming Huo 36 1 0 08 Jan 2024
From Complexity to Clarity: Analytical Expressions of Deep Neural Network Weights via Clifford's Geometric Algebra and Convexity Mert Pilanci 34 2 0 28 Sep 2023
Can predictive models be used for causal inference? Maximilian Pichler F. Hartig OOD CML 32 3 0 18 Jun 2023
ReLU soothes the NTK condition number and accelerates optimization for wide neural networks Chaoyue Liu Like Hui MLT 22 9 0 15 May 2023
Ensemble Multi-Quantiles: Adaptively Flexible Distribution Prediction for Uncertainty Quantification Xing Yan Yonghua Su Wenxuan Ma UQCV 32 1 0 26 Nov 2022