no code implementations • 24 Jan 2024 • Ismail Nejjar, Gaetan Frusque, Florent Forest, Olga Fink
Our approach serves a dual purpose: providing a measure of confidence in predictions and acting as a regularization of the embedding space.
no code implementations • 5 Dec 2023 • Gaëtan Frusque, Ismail Nejjar, Majid Nabavi, Olga Fink
The Health Index (HI) is crucial for evaluating system health, aiding tasks like anomaly detection and predicting remaining useful life for systems demanding high safety and reliability.
Semi-supervised Anomaly Detection Supervised Anomaly Detection
1 code implementation • NeurIPS 2023 • Hao Dong, Ismail Nejjar, Han Sun, Eleni Chatzi, Olga Fink
In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions.
1 code implementation • 23 Mar 2023 • 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.
no code implementations • 3 Feb 2023 • Ismail Nejjar, Fabian Geissmann, Mengjie Zhao, Cees Taal, Olga Fink
Domain adaptation (DA) methods aim to address the domain shift problem by extracting domain invariant features.
1 code implementation • CVPR 2023 • 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.
1 code implementation • 8 Mar 2021 • Mohammadhossein Bahari, Ismail Nejjar, Alexandre Alahi
On the other hand, recent works use data-driven approaches which can learn complex interactions from the data leading to superior performance.