Statistical Learning
Navigating the Maze of Explainable AI: A Systematic Approach to Evaluating Methods and Metrics Lukas Klein 1,2,3 Carsten Lüth
Explainable AI (XAI) is a rapidly growing domain with a myriad of proposed methods as well as metrics aiming to evaluate their efficacy.
Back to the Continuous Attractor
Continuous attractors offer a unique class of solutions for storing continuous-valued variables in recurrent system states for indefinitely long time intervals.
Appendices Table of Contents
Other concurrent works address, e.g., learning from soft interventions with polynomial mixing [
Cross-modal Representation Flattening for Multi-modal Domain Generalization Yunfeng Fan
We implement this method by distilling and optimizing generalizable interpolated representations and assigning distinct weights for each modality considering their divergent generalization capabilities.
Training Data Attribution via Approximate Unrolling
Many training data attribution (TDA) methods aim to estimate how a model's behavior would change if one or more data points were removed from the training set.