136 citations · 448 across the 41 of their papers we have counts for
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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…
Unified Framework for Pre-trained Neural Network Compression via Decomposition and Optimized Rank Selection
Ali Aghababaei-Harandi, Massih-Reza Amini
Despite their high accuracy, complex neural networks demand significant computational resources, posing challenges for deployment on resource constrained devices such as mobile pho…
Classification Tree-based Active Learning: A Wrapper Approach
Ashna Jose, Emilie Devijver, Massih-Reza Amini +2
Supervised machine learning often requires large training sets to train accurate models, yet obtaining large amounts of labeled data is not always feasible. Hence, it becomes cruci…
Exploring Contrastive Learning for Long-Tailed Multi-Label Text Classification
Alexandre Audibert, Aurélien Gauffre, Massih-Reza Amini
Learning an effective representation in multi-label text classification (MLTC) is a significant challenge in NLP. This challenge arises from the inherent complexity of the task, wh…