74 citations · 92 across the 11 of their papers we have counts for
12 papers
Rethinking Object-Centric Representations for Video Dynamics Modeling
Amaury Wei, Ismail Nejjar, Olga Fink
Learning to decompose videos into persistent objects is a fundamental challenge in unsupervised object-centric representation learning. Despite recent progress, existing methods st…
From Physics to Machine Learning and Back: Part II - Learning and Observational Bias in PHM
Olga Fink, Ismail Nejjar, Vinay Sharma +13
Prognostics and Health Management ensures the reliability, safety, and efficiency of complex engineered systems by enabling fault detection, anticipating equipment failures, and op…
DynAlign: Unsupervised Dynamic Taxonomy Alignment for Cross-Domain Segmentation
Han Sun, Rui Gong, Ismail Nejjar +1
Current unsupervised domain adaptation (UDA) methods for semantic segmentation typically assume identical class labels between the source and target domains. This assumption ignore…
Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation Framework
Ismail Nejjar, Hao Dong, Olga Fink
Open-set Domain Adaptation (OSDA) aims to adapt a model from a labeled source domain to an unlabeled target domain, where novel classes - also referred to as target-private unknown…
Efficient Unsupervised Domain Adaptation Regression for Spatial-Temporal Sensor Fusion
Keivan Faghih Niresi, Ismail Nejjar, Olga Fink
The growing deployment of low-cost, distributed sensor networks in environmental and biomedical domains has enabled continuous, large-scale health monitoring. However, these system…
IM-Context: In-Context Learning for Imbalanced Regression Tasks
Ismail Nejjar, Faez Ahmed, Olga Fink
Regression models often fail to generalize effectively in regions characterized by highly imbalanced label distributions. Previous methods for deep imbalanced regression rely on gr…
Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression
Ismail Nejjar, Gaetan Frusque, Florent Forest +1
Unsupervised Domain Adaptation for Regression (UDAR) aims to adapt models from a labeled source domain to an unlabeled target domain for regression tasks. Traditional feature align…
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion
Gaëtan Frusque, Ismail Nejjar, Majid Nabavi +1
The Health Index (HI) is crucial for evaluating system health and is important for tasks like anomaly detection and Remaining Useful Life (RUL) prediction of safety-critical system…
SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
Hao Dong, Ismail Nejjar, Han Sun +2
In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions. Generalizing to…
DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices
Ismail Nejjar, Qin Wang, Olga Fink
Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems. Recent work…
Domain Adaptation via Alignment of Operation Profile for Remaining Useful Lifetime Prediction
Ismail Nejjar, Fabian Geissmann, Mengjie Zhao +2
Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the repres…
Injecting Knowledge in Data-driven Vehicle Trajectory Predictors
Mohammadhossein Bahari, Ismail Nejjar, Alexandre Alahi
Vehicle trajectory prediction tasks have been commonly tackled from two distinct perspectives: either with knowledge-driven methods or more recently with data-driven ones. On the o…