SPADE: Sparsity-Guided Debugging for Deep Neural Networks

SPADE: Sparsity-Guided Debugging for Deep Neural Networks

6 October 2023

Arshia Soltani Moakhar

Eugenia Iofinova

Dan Alistarh

Papers citing "SPADE: Sparsity-Guided Debugging for Deep Neural Networks"

9 / 9 papers shown

Title
AtP*: An efficient and scalable method for localizing LLM behaviour to components János Kramár Tom Lieberum Rohin Shah Neel Nanda KELM 45 42 0 01 Mar 2024
Sparse Linear Concept Discovery Models Konstantinos P. Panousis Dino Ienco Diego Marcos 31 15 0 21 Aug 2023
Finding Neurons in a Haystack: Case Studies with Sparse Probing Wes Gurnee Neel Nanda Matthew Pauly Katherine Harvey Dmitrii Troitskii Dimitris Bertsimas MILM 160 188 0 02 May 2023
Interpretability in the Wild: a Circuit for Indirect Object Identification in GPT-2 small Kevin Wang Alexandre Variengien Arthur Conmy Buck Shlegeris Jacob Steinhardt 212 496 0 01 Nov 2022
Polysemanticity and Capacity in Neural Networks Adam Scherlis Kshitij Sachan Adam Jermyn Joe Benton Buck Shlegeris MILM 135 25 0 04 Oct 2022
Metrics for saliency map evaluation of deep learning explanation methods T. Gomez Thomas Fréour Harold Mouchère XAI FAtt 69 41 0 31 Jan 2022
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks Itay Hubara Brian Chmiel Moshe Island Ron Banner S. Naor Daniel Soudry 50 111 0 16 Feb 2021
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand M. Andreetto Hartwig Adam 3DH 950 20,567 0 17 Apr 2017
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 254 3,684 0 28 Feb 2017