Revealing Model Biases: Assessing Deep Neural Networks via Recovered Sample Analysis

10 June 2023

Papers citing "Revealing Model Biases: Assessing Deep Neural Networks via Recovered Sample Analysis"

9 / 9 papers shown

Title
Simplicity Bias in 1-Hidden Layer Neural Networks Depen Morwani Jatin Batra Prateek Jain Praneeth Netrapalli 69 21 0 01 Feb 2023
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks Sravanti Addepalli Anshul Nasery R. Venkatesh Babu Praneeth Netrapalli Prateek Jain AAML 88 3 0 04 Oct 2022
Reconstructing Training Data from Trained Neural Networks Niv Haim Gal Vardi Gilad Yehudai Ohad Shamir Michal Irani 89 141 0 15 Jun 2022
Discover the Unknown Biased Attribute of an Image Classifier Zhiheng Li Chenliang Xu 71 50 0 29 Apr 2021
The Pitfalls of Simplicity Bias in Neural Networks Harshay Shah Kaustav Tamuly Aditi Raghunathan Prateek Jain Praneeth Netrapalli AAML 69 361 0 13 Jun 2020
Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data Charles H. Martin Tongsu Peng Peng Michael W. Mahoney 83 110 0 17 Feb 2020
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks Kaifeng Lyu Jian Li 98 336 0 13 Jun 2019
Heavy-Tailed Universality Predicts Trends in Test Accuracies for Very Large Pre-Trained Deep Neural Networks Charles H. Martin Michael W. Mahoney 44 56 0 24 Jan 2019
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 282 14,963 1 21 Dec 2013