2 citations · 3 across the 4 of their papers we have counts for
3 papers · 1 filter
eXIAA: eXplainable Injections for Adversarial Attack
Leonardo Pesce, Jiawen Wei, Gianmarco Mengaldo
Post-hoc explainability methods are a subset of Machine Learning (ML) that aim to provide a reason for why a model behaves in a certain way. In this paper, we show a new black-box…
XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change
Jiawen Wei, Aniruddha Bora, Vivek Oommen +7
Extreme weather events are increasing in frequency and intensity due to climate change. This, in turn, is exacting a significant toll in communities worldwide. While prediction ski…
Revisiting the robustness of post-hoc interpretability methods
Jiawen Wei, Hugues Turbé, Gianmarco Mengaldo
Post-hoc interpretability methods play a critical role in explainable artificial intelligence (XAI), as they pinpoint portions of data that a trained deep learning model deemed imp…