244 citations · 355 across the 14 of their papers we have counts for
23 papers
Test-Time Adaptation with Principal Component Analysis
Thomas Cordier, Victor Bouvier, Gilles Hénaff +1
Machine Learning models are prone to fail when test data are different from training data, a situation often encountered in real applications known as distribution shift. While sti…
Few-Shot Image Classification Benchmarks are Too Far From Reality: Build Back Better with Semantic Task Sampling
Etienne Bennequin, Myriam Tami, Antoine Toubhans +1
Every day, a new method is published to tackle Few-Shot Image Classification, showing better and better performances on academic benchmarks. Nevertheless, we observe that these cur…
Minority Class Oriented Active Learning for Imbalanced Datasets
Umang Aggarwal, Adrian Popescu, Céline Hudelot
Active learning aims to optimize the dataset annotation process when resources are constrained. Most existing methods are designed for balanced datasets. Their practical applicabil…
A Comparative Study of Calibration Methods for Imbalanced Class Incremental Learning
Umang Aggarwal, Adrian Popescu, Eden Belouadah +1
Deep learning approaches are successful in a wide range of AI problems and in particular for visual recognition tasks. However, there are still open problems among which is the cap…
Optimizing Active Learning for Low Annotation Budgets
Umang Aggarwal, Adrian Popescu, Céline Hudelot
When we can not assume a large amount of annotated data , active learning is a good strategy. It consists in learning a model on a small amount of annotated data (annotation budget…
Demystifying Drug Repurposing Domain Comprehension with Knowledge Graph Embedding
Edoardo Ramalli, Alberto Parravicini, Guido Walter Di Donato +3
Drug repurposing is more relevant than ever due to drug development's rising costs and the need to respond to emerging diseases quickly. Knowledge graph embedding enables drug repu…