How Does a Neural Network's Architecture Impact Its Robustness to Noisy
Labels?

How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?

23 December 2020

John P. Dickerson

Jimmy Ba

Papers citing "How Does a Neural Network's Architecture Impact Its Robustness to Noisy Labels?"

7 / 7 papers shown

Title
Strong inductive biases provably prevent harmless interpolation Michael Aerni Marco Milanta Konstantin Donhauser Fanny Yang 35 9 0 18 Jan 2023
Leveraging Unlabeled Data to Track Memorization Mahsa Forouzesh Hanie Sedghi Patrick Thiran NoLa TDI 34 4 0 08 Dec 2022
Graph Neural Networks are Dynamic Programmers Andrew Dudzik Petar Velickovic 34 63 0 29 Mar 2022
Detecting Corrupted Labels Without Training a Model to Predict Zhaowei Zhu Zihao Dong Yang Liu NoLa 149 62 0 12 Oct 2021
Curriculum Loss: Robust Learning and Generalization against Label Corruption Yueming Lyu Ivor W. Tsang NoLa 61 172 0 24 May 2019
Representation Learning on Graphs with Jumping Knowledge Networks Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi Stefanie Jegelka GNN 270 1,944 0 09 Jun 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017