Improving performance of deep learning models with axiomatic attribution
priors and expected gradients

Improving performance of deep learning models with axiomatic attribution priors and expected gradients

25 June 2019

Joseph D. Janizek

Pascal Sturmfels

Scott M. Lundberg

Papers citing "Improving performance of deep learning models with axiomatic attribution priors and expected gradients"

17 / 17 papers shown

Title
Explanation Space: A New Perspective into Time Series Interpretability Shahbaz Rezaei Xin Liu AI4TS 34 1 0 02 Sep 2024
Interpretable Network Visualizations: A Human-in-the-Loop Approach for Post-hoc Explainability of CNN-based Image Classification Matteo Bianchi Antonio De Santis Andrea Tocchetti Marco Brambilla MILM FAtt 32 1 0 06 May 2024
Going Beyond XAI: A Systematic Survey for Explanation-Guided Learning Yuyang Gao Siyi Gu Junji Jiang S. Hong Dazhou Yu Liang Zhao 29 39 0 07 Dec 2022
On the Robustness of Explanations of Deep Neural Network Models: A Survey Amlan Jyoti Karthik Balaji Ganesh Manoj Gayala Nandita Lakshmi Tunuguntla Sandesh Kamath V. Balasubramanian XAI FAtt AAML 32 4 0 09 Nov 2022
A model-agnostic approach for generating Saliency Maps to explain inferred decisions of Deep Learning Models S. Karatsiolis A. Kamilaris FAtt 29 1 0 19 Sep 2022
Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement Xiaoting Shao Karl Stelzner Kristian Kersting CML DRL 22 3 0 01 Feb 2022
Temporal Dependencies in Feature Importance for Time Series Predictions Kin Kwan Leung Clayton Rooke Jonathan Smith S. Zuberi M. Volkovs OOD AI4TS 25 24 0 29 Jul 2021
Towards Robust Classification Model by Counterfactual and Invariant Data Generation C. Chang George Adam Anna Goldenberg OOD CML 24 31 0 02 Jun 2021
Towards Rigorous Interpretations: a Formalisation of Feature Attribution Darius Afchar Romain Hennequin Vincent Guigue FAtt 31 20 0 26 Apr 2021
Shapley Explanation Networks Rui Wang Xiaoqian Wang David I. Inouye TDI FAtt 19 44 0 06 Apr 2021
Efficient Explanations from Empirical Explainers Robert Schwarzenberg Nils Feldhus Sebastian Möller FAtt 32 9 0 29 Mar 2021
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 36 241 0 21 Nov 2020
Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers Hanjie Chen Yangfeng Ji AAML VLM 13 62 0 01 Oct 2020
Captum: A unified and generic model interpretability library for PyTorch Narine Kokhlikyan Vivek Miglani Miguel Martin Edward Wang B. Alsallakh ... Alexander Melnikov Natalia Kliushkina Carlos Araya Siqi Yan Orion Reblitz-Richardson FAtt 29 821 0 16 Sep 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 30 143 0 10 Feb 2020
Making deep neural networks right for the right scientific reasons by interacting with their explanations P. Schramowski Wolfgang Stammer Stefano Teso Anna Brugger Xiaoting Shao Hans-Georg Luigs Anne-Katrin Mahlein Kristian Kersting 32 207 0 15 Jan 2020
Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms Z. Q. Lin M. Shafiee S. Bochkarev Michael St. Jules Xiao Yu Wang A. Wong FAtt 26 80 0 16 Oct 2019