1 citations · 2 across the 5 of their papers we have counts for
6 papers
S2S-JEPA: Predicting the Predictable at Subseasonal-to-Seasonal Timescales
Chenyu Dong, Gianmarco Mengaldo
The subseasonal-to-seasonal (S2S) timescale, roughly from two weeks to two months ahead, is a critical forecast window for sectors such as agriculture, energy, and water management…
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…
Explainability matters: The effect of liability rules on the healthcare sector
Jiawen Wei, Elena Verona, Andrea Bertolini +1
Explainability, the capability of an artificial intelligence system (AIS) to explain its outcomes in a manner that is comprehensible to human beings at an acceptable level, has bee…
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…
Spatio-temporal Dynamical Indices for Complex Systems
Chenyu Dong, Gabriele Messori, Davide Faranda +3
Complex systems span multiple spatial and temporal scales, making their dynamics challenging to understand and predict. This challenge is especially daunting when one wants to stud…
Time-Lagged Recurrence: a data-driven method to estimate the predictability of dynamical systems
Chenyu Dong, Davide Faranda, Adriano Gualandi +2
Nonlinear dynamical systems are ubiquitous in nature and they are hard to forecast. Not only they may be sensitive to small perturbations in their initial conditions, but they are…