1 citations · 2 across the 7 of their papers we have counts for
10 papers
TSPFN: A Temporal Tabular Foundation Model for Physiological Time Series Classification
Jérémie Stym-Popper, Clément Rambour, Federica Granese +2
Designing models that generalize effectively in low- to medium-data regimes remains a primary challenge in medical machine learning, particularly for physiological time-series clas…
DAFTED: Decoupled Asymmetric Fusion of Tabular and Echocardiographic Data for Cardiac Hypertension Diagnosis
Jérémie Stym-Popper, Nathan Painchaud, Clément Rambour +3
Multimodal data fusion is a key approach for enhancing diagnosis in medical applications. We propose an asymmetric fusion strategy starting from a primary modality and integrating…
CLIPTTA: Robust Contrastive Vision-Language Test-Time Adaptation
Marc Lafon, Gustavo Adolfo Vargas Hakim, Clément Rambour +2
Vision-language models (VLMs) like CLIP exhibit strong zero-shot capabilities but often fail to generalize under distribution shifts. Test-time adaptation (TTA) allows models to up…
ViLU: Learning Vision-Language Uncertainties for Failure Prediction
Marc Lafon, Yannis Karmim, Julio Silva-Rodríguez +6
Reliable Uncertainty Quantification (UQ) and failure prediction remain open challenges for Vision-Language Models (VLMs). We introduce ViLU, a new Vision-Language Uncertainty quant…
Supra-Laplacian Encoding for Transformer on Dynamic Graphs
Yannis Karmim, Marc Lafon, Raphael Fournier S'niehotta +1
Fully connected Graph Transformers (GT) have rapidly become prominent in the static graph community as an alternative to Message-Passing models, which suffer from a lack of express…
Temporal receptive field in dynamic graph learning: A comprehensive analysis
Yannis Karmim, Leshanshui Yang, Raphaël Fournier S'Niehotta +3
Dynamic link prediction is a critical task in the analysis of evolving networks, with applications ranging from recommender systems to economic exchanges. However, the concept of t…
ITEM: Improving Training and Evaluation of Message-Passing based GNNs for top-k recommendation
Yannis Karmim, Elias Ramzi, Raphaël Fournier-S'niehotta +1
Graph Neural Networks (GNNs), especially message-passing-based models, have become prominent in top-k recommendation tasks, outperforming matrix factorization models due to their a…
GalLoP: Learning Global and Local Prompts for Vision-Language Models
Marc Lafon, Elias Ramzi, Clément Rambour +2
Prompt learning has been widely adopted to efficiently adapt vision-language models (VLMs), e.g. CLIP, for few-shot image classification. Despite their success, most prompt learnin…
Energy Correction Model in the Feature Space for Out-of-Distribution Detection
Marc Lafon, Clément Rambour, Nicolas Thome
In this work, we study the out-of-distribution (OOD) detection problem through the use of the feature space of a pre-trained deep classifier. We show that learning the density of i…
Optimization of Rank Losses for Image Retrieval
Elias Ramzi, Nicolas Audebert, Clément Rambour +3
In image retrieval, standard evaluation metrics rely on score ranking, \eg average precision (AP), recall at k (R@k), normalized discounted cumulative gain (NDCG). In this work we…