20 citations · 23 across the 5 of their papers we have counts for
5 papers
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…
A Comprehensive Review on Financial Explainable AI
Wei Jie Yeo, Wihan van der Heever, Rui Mao +3
The success of artificial intelligence (AI), and deep learning models in particular, has led to their widespread adoption across various industries due to their ability to process…
Online data-driven changepoint detection for high-dimensional dynamical systems
Sen Lin, Gianmarco Mengaldo, Romit Maulik
The detection of anomalies or transitions in complex dynamical systems is of critical importance to various applications. In this study, we propose the use of machine learning to d…
Reduced order modeling for spectral element methods: current developments in Nektar++ and further perspectives
Martin W. Hess, Andrea Lario, Gianmarco Mengaldo +1
In this paper, we present recent efforts to develop reduced order modeling (ROM) capabilities for spectral element methods (SEM). Namely, we detail the implementation of ROM for bo…
Efficient high-dimensional variational data assimilation with machine-learned reduced-order models
Romit Maulik, Vishwas Rao, Jiali Wang +6
Data assimilation (DA) in the geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical we…