WWW: A Unified Framework for Explaining What, Where and Why of Neural Networks by Interpretation of Neuron Concepts

29 February 2024

Papers citing "WWW: A Unified Framework for Explaining What, Where and Why of Neural Networks by Interpretation of Neuron Concepts"

7 / 7 papers shown

Title
LINe: Out-of-Distribution Detection by Leveraging Important Neurons Yong Hyun Ahn Gyeong-Moon Park Seong Tae Kim OODD 105 31 0 24 Mar 2023
Natural Language Descriptions of Deep Visual Features Evan Hernandez Sarah Schwettmann David Bau Teona Bagashvili Antonio Torralba Jacob Andreas MILM 194 116 0 26 Jan 2022
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information Yang Zhang Ashkan Khakzar Yawei Li Azade Farshad Seong Tae Kim Nassir Navab FAtt XAI 44 27 0 04 Oct 2021
A Survey on Neural Network Interpretability Yu Zhang Peter Tiño A. Leonardis K. Tang FaML XAI 134 654 0 28 Dec 2020
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI Alon Jacovi Ana Marasović Tim Miller Yoav Goldberg 241 422 0 15 Oct 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 225 3,658 0 28 Feb 2017
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 282 39,170 0 01 Sep 2014