Where do Models go Wrong? Parameter-Space Saliency Maps for
Explainability

Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability

3 August 2021

Furong Huang

Papers citing "Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability"

7 / 7 papers shown

Title
Talking Nonsense: Probing Large Language Models' Understanding of Adversarial Gibberish Inputs Valeriia Cherepanova James Zou AAML 33 4 0 26 Apr 2024
KS-Lottery: Finding Certified Lottery Tickets for Multilingual Language Models Fei Yuan Chang Ma Shuai Yuan Qiushi Sun Lei Li 39 3 0 05 Feb 2024
A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning Valeriia Cherepanova Roman Levin Gowthami Somepalli Jonas Geiping C. Bayan Bruss Andrew Gordon Wilson Tom Goldstein Micah Goldblum 28 18 0 10 Nov 2023
Understanding Parameter Saliency via Extreme Value Theory Shuo Wang Issei Sato AAML FAtt 21 0 0 27 Oct 2023
Identification of Attack-Specific Signatures in Adversarial Examples Hossein Souri Pirazh Khorramshahi Chun Pong Lau Micah Goldblum Rama Chellappa AAML MLAU 43 4 0 13 Oct 2021
Efficient Sharpness-aware Minimization for Improved Training of Neural Networks Jiawei Du Hanshu Yan Jiashi Feng Qiufeng Wang Liangli Zhen Rick Siow Mong Goh Vincent Y. F. Tan AAML 113 132 0 07 Oct 2021
Language Models as Knowledge Bases? Fabio Petroni Tim Rocktaschel Patrick Lewis A. Bakhtin Yuxiang Wu Alexander H. Miller Sebastian Riedel KELM AI4MH 417 2,588 0 03 Sep 2019