Simple and Effective Regularization Methods for Training on Noisily
Labeled Data with Generalization Guarantee

Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee

27 May 2019

Dingli Yu

Papers citing "Simple and Effective Regularization Methods for Training on Noisily Labeled Data with Generalization Guarantee"

5 / 5 papers shown

Title
Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning A. Feder Cooper Emanuel Moss Benjamin Laufer Helen Nissenbaum MLAU 26 85 0 10 Feb 2022
Knowledge Distillation Beyond Model Compression F. Sarfraz Elahe Arani Bahram Zonooz 12 40 0 03 Jul 2020
Learning Not to Learn in the Presence of Noisy Labels Liu Ziyin Blair Chen Ru Wang Paul Pu Liang Ruslan Salakhutdinov Louis-Philippe Morency Masahito Ueda NoLa 26 18 0 16 Feb 2020
Beyond Linearization: On Quadratic and Higher-Order Approximation of Wide Neural Networks Yu Bai J. Lee 24 116 0 03 Oct 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks Mingchen Li Mahdi Soltanolkotabi Samet Oymak NoLa 47 351 0 27 Mar 2019