11 citations · 47 across the 48 of their papers we have counts for
5 papers · 2 filters
Quanda: An Interpretability Toolkit for Training Data Attribution Evaluation and Beyond
Dilyara Bareeva, Galip Ümit Yolcu, Anna Hedström +4
In recent years, training data attribution (TDA) methods have emerged as a promising direction for the interpretability of neural networks. While research around TDA is thriving, l…
PINNfluence: Interpreting PINNs through Influence Functions
Aleksander Krasowski, Jonas R. Naujoks, Moritz Weckbecker +5
Physics-informed neural networks (PINNs) have emerged as a powerful deep learning approach for solving partial differential equations (PDEs) in the physical sciences, yet their beh…
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression
Dilyara Bareeva, Maximilian Dreyer, Frederik Pahde +2
Deep Neural Networks are prone to learning and relying on spurious correlations in the training data, which, for high-risk applications, can have fatal consequences. Various approa…
Sparse, Efficient and Explainable Data Attribution with DualXDA
Galip Ümit Yolcu, Moritz Weckbecker, Thomas Wiegand +2
Data Attribution (DA) is an emerging approach in the field of eXplainable Artificial Intelligence (XAI), aiming to identify influential training datapoints which determine model ou…
Explaining Predictive Uncertainty by Exposing Second-Order Effects
Florian Bley, Sebastian Lapuschkin, Wojciech Samek +1
Explainable AI has brought transparency into complex ML blackboxes, enabling, in particular, to identify which features these models use for their predictions. So far, the question…