Search Results for author: Ismail Nejjar

Found 7 papers, 4 papers with code

Uncertainty-Guided Alignment for Unsupervised Domain Adaptation in Regression

no code implementations24 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.

regression Unsupervised Domain Adaptation

Semi-Supervised Health Index Monitoring with Feature Generation and Fusion

no code implementations5 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

SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization

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.

Contrastive Learning Domain Generalization

DARE-GRAM : Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

1 code implementation23 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.

regression Unsupervised Domain Adaptation

DARE-GRAM: Unsupervised Domain Adaptation Regression by Aligning Inverse Gram Matrices

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.

regression Unsupervised Domain Adaptation

Injecting Knowledge in Data-driven Vehicle Trajectory Predictors

1 code implementation8 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.

Model Predictive Control Trajectory Prediction

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