2024 "explainable ai" Papers
20 papers found
Accelerating the Global Aggregation of Local Explanations
Alon Mor, Yonatan Belinkov, Benny Kimelfeld
Attribution-based Explanations that Provide Recourse Cannot be Robust
Hidde Fokkema, Rianne de Heide, Tim van Erven
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles
Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer et al.
CGS-Mask: Making Time Series Predictions Intuitive for All
Feng Lu, Wei Li, Yifei Sun et al.
Counterfactual Metarules for Local and Global Recourse
Tom Bewley, Salim I. Amoukou, Saumitra Mishra et al.
EiG-Search: Generating Edge-Induced Subgraphs for GNN Explanation in Linear Time
Shengyao Lu, Bang Liu, Keith Mills et al.
Enhance Sketch Recognition’s Explainability via Semantic Component-Level Parsing
Guangming Zhu, Siyuan Wang, Tianci Wu et al.
Faithful Model Explanations through Energy-Constrained Conformal Counterfactuals
Patrick Altmeyer, Mojtaba Farmanbar, Arie Van Deursen et al.
Gaussian Process Neural Additive Models
Wei Zhang, Brian Barr, John Paisley
Generating In-Distribution Proxy Graphs for Explaining Graph Neural Networks
Zhuomin Chen, Jiaxing Zhang, Jingchao Ni et al.
Good Teachers Explain: Explanation-Enhanced Knowledge Distillation
Amin Parchami, Moritz Böhle, Sukrut Rao et al.
Graph Neural Network Explanations are Fragile
Jiate Li, Meng Pang, Yun Dong et al.
Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning
Tom Nuno Wolf, Fabian Bongratz, Anne-Marie Rickmann et al.
Learning Performance Maximizing Ensembles with Explainability Guarantees
Vincent Pisztora, Jia Li
Manifold Integrated Gradients: Riemannian Geometry for Feature Attribution
Eslam Zaher, Maciej Trzaskowski, Quan Nguyen et al.
On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box
Yi Cai, Gerhard Wunder
Position: Do Not Explain Vision Models Without Context
Paulina Tomaszewska, Przemyslaw Biecek
Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations for Vision Foundation Models
Hengyi Wang, Shiwei Tan, Hao Wang
Towards More Faithful Natural Language Explanation Using Multi-Level Contrastive Learning in VQA
Chengen Lai, Shengli Song, Shiqi Meng et al.
Using Stratified Sampling to Improve LIME Image Explanations
Muhammad Rashid, Elvio G. Amparore, Enrico Ferrari et al.