136 citations · 248 across the 13 of their papers we have counts for
22 papers
Multi-Label Contrastive Learning : A Comprehensive Study
Alexandre Audibert, Aurélien Gauffre, Massih-Reza Amini
Multi-label classification, which involves assigning multiple labels to a single input, has emerged as a key area in both research and industry due to its wide-ranging applications…
A Unified Contrastive Loss for Self-Training
Aurelien Gauffre, Julien Horvat, Massih-Reza Amini
Self-training methods have proven to be effective in exploiting abundant unlabeled data in semi-supervised learning, particularly when labeled data is scarce. While many of these a…
Learning over No-Preferred and Preferred Sequence of Items for Robust Recommendation (Extended Abstract)
Aleksandra Burashnikova, Yury Maximov, Marianne Clausel +3
This paper is an extended version of [Burashnikova et al., 2021, arXiv: 2012.06910], where we proposed a theoretically supported sequential strategy for training a large-scale Reco…
Self Semi Supervised Neural Architecture Search for Semantic Segmentation
Loïc Pauletto, Massih-Reza Amini, Nicolas Winckler
In this paper, we propose a Neural Architecture Search strategy based on self supervision and semi-supervised learning for the task of semantic segmentation. Our approach builds an…
Multi-class Probabilistic Bounds for Self-learning
Vasilii Feofanov, Emilie Devijver, Massih-Reza Amini
Self-learning is a classical approach for learning with both labeled and unlabeled observations which consists in giving pseudo-labels to unlabeled training instances with a confid…
Self-Learning for Received Signal Strength Map Reconstruction with Neural Architecture Search
Aleksandra Malkova, Loic Pauletto, Christophe Villien +2
In this paper, we present a Neural Network (NN) model based on Neural Architecture Search (NAS) and self-learning for received signal strength (RSS) map reconstruction out of spars…