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Statistical Guarantees for Regularized Neural Networks

30 May 2020

Papers citing "Statistical Guarantees for Regularized Neural Networks"

26 / 26 papers shown

Title
Neural Drift Estimation for Ergodic Diffusions: Non-parametric Analysis and Numerical Exploration Simone Di Gregorio Francesco Iafrate 17 0 0 30 May 2025
Regularization can make diffusion models more efficient Mahsa Taheri Johannes Lederer 171 0 0 13 Feb 2025
Adversarially robust generalization theory via Jacobian regularization for deep neural networks Dongya Wu Xin Li AAML 107 0 0 17 Dec 2024
Fast Training of Sinusoidal Neural Fields via Scaling Initialization Taesun Yeom Sangyoon Lee Jaeho Lee 110 3 0 07 Oct 2024
On the estimation rate of Bayesian PINN for inverse problems Yi Sun Debarghya Mukherjee Yves Atchadé PINN 112 1 0 21 Jun 2024
How many samples are needed to train a deep neural network? Pegah Golestaneh Mahsa Taheri Johannes Lederer 76 4 0 26 May 2024
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective Yue Xing Xiaofeng Lin Qifan Song Yi Tian Xu Belinda Zeng Guang Cheng SSL 63 0 0 26 Jan 2024
Statistical learning by sparse deep neural networks Felix Abramovich BDL 77 1 0 15 Nov 2023
A statistical perspective on algorithm unrolling models for inverse problems Yves Atchadé Xinru Liu Qiuyun Zhu 53 0 0 10 Nov 2023
Adversarial Training with Generated Data in High-Dimensional Regression: An Asymptotic Study Yue Xing 46 0 0 21 Jun 2023
Distribution Estimation of Contaminated Data via DNN-based MoM-GANs Fang Xie Lihu Xu Qiuran Yao Huiming Zhang 55 0 0 28 Dec 2022
Statistical guarantees for sparse deep learning Johannes Lederer 40 11 0 11 Dec 2022
Nonparametric regression with modified ReLU networks A. Beknazaryan Hailin Sang 52 0 0 17 Jul 2022
Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks Mahsa Taheri Fang Xie Johannes Lederer 52 0 0 09 May 2022
A PAC-Bayes oracle inequality for sparse neural networks Maximilian F. Steffen Mathias Trabs UQCV 65 2 0 26 Apr 2022
Deep Learning meets Nonparametric Regression: Are Weight-Decayed DNNs Locally Adaptive? Kaiqi Zhang Yu Wang 118 12 0 20 Apr 2022
Non-Asymptotic Guarantees for Robust Statistical Learning under Infinite Variance Assumption Lihu Xu Fang Yao Qiuran Yao Huiming Zhang 69 11 0 10 Jan 2022
Regularization and Reparameterization Avoid Vanishing Gradients in Sigmoid-Type Networks Leni Ven Johannes Lederer ODL 54 6 0 04 Jun 2021
Neural networks with superexpressive activations and integer weights A. Beknazaryan 64 6 0 20 May 2021
Analytic function approximation by path norm regularized deep networks A. Beknazaryan 43 2 0 05 Apr 2021
$Function approximation by deep neural networks with parameters $\{0,\pm \frac{1}{2}, \pm 1, 2\}$$ Function approximation by deep neural networks with parameters $\{0,\pm \frac{1}{2}, \pm 1, 2\}$ A. Beknazaryan 41 5 0 15 Mar 2021
Activation Functions in Artificial Neural Networks: A Systematic Overview Johannes Lederer FAtt AI4CE 66 47 0 25 Jan 2021
Optimization Landscapes of Wide Deep Neural Networks Are Benign Johannes Lederer 75 8 0 02 Oct 2020
Risk Bounds for Robust Deep Learning Johannes Lederer OOD 59 16 0 14 Sep 2020
HALO: Learning to Prune Neural Networks with Shrinkage Skyler Seto M. Wells Wenyu Zhang 54 0 0 24 Aug 2020
Layer Sparsity in Neural Networks Mohamed Hebiri Johannes Lederer 92 10 0 28 Jun 2020