1 citations · 2 across the 5 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2025
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…
cs.LG2025
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…