activity
20192026
most citedAmplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning

20 citations · 29 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis

Alban Puech, Matteo Mazzonelli, Tamara R. Govindasamy +19

Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power system analysis, where strict…

cs.CV2024

VFA: Vision Frequency Analysis of Foundation Models and Human

Mohammad-Javad Darvishi-Bayazi, Md Rifat Arefin, Jocelyn Faubert +1

Machine learning models often struggle with distribution shifts in real-world scenarios, whereas humans exhibit robust adaptation. Models that better align with human perception ma…

cs.LG2023★ 20 cited

Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning

Mohammad-Javad Darvishi-Bayazi, Mohammad Sajjad Ghaemi, Timothee Lesort +3

Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With the advancement of artificial intelligen…

cs.LG2022★ 9 cited

WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series

Jean-Christophe Gagnon-Audet, Kartik Ahuja, Mohammad-Javad Darvishi-Bayazi +3

Machine learning models often fail to generalize well under distributional shifts. Understanding and overcoming these failures have led to a research field of Out-of-Distribution (…

cs.LG2019

Generalizing to unseen domains via distribution matching

Isabela Albuquerque, João Monteiro, Mohammad Darvishi +2

Supervised learning results typically rely on assumptions of i.i.d. data. Unfortunately, those assumptions are commonly violated in practice. In this work, we tackle such problem b…