40 citations · 79 across the 7 of their papers we have counts for
7 papers
Image Compression using only Attention based Neural Networks
Natacha Luka, Romain Negrel, David Picard
In recent research, Learned Image Compression has gained prominence for its capacity to outperform traditional handcrafted pipelines, especially at low bit-rates. While existing me…
Domain-Aware Augmentations for Unsupervised Online General Continual Learning
Nicolas Michel, Romain Negrel, Giovanni Chierchia +1
Continual Learning has been challenging, especially when dealing with unsupervised scenarios such as Unsupervised Online General Continual Learning (UOGCL), where the learning agen…
New metrics for analyzing continual learners
Nicolas Michel, Giovanni Chierchia, Romain Negrel +2
Deep neural networks have shown remarkable performance when trained on independent and identically distributed data from a fixed set of classes. However, in real-world scenarios, i…
Learning Representations on the Unit Sphere: Investigating Angular Gaussian and von Mises-Fisher Distributions for Online Continual Learning
Nicolas Michel, Giovanni Chierchia, Romain Negrel +1
We use the maximum a posteriori estimation principle for learning representations distributed on the unit sphere. We propose to use the angular Gaussian distribution, which corresp…
Contrastive Learning for Online Semi-Supervised General Continual Learning
Nicolas Michel, Romain Negrel, Giovanni Chierchia +1
We study Online Continual Learning with missing labels and propose SemiCon, a new contrastive loss designed for partly labeled data. We demonstrate its efficiency by devising a mem…
Online convex optimization and no-regret learning: Algorithms, guarantees and applications
E. Veronica Belmega, Panayotis Mertikopoulos, Romain Negrel +1
Spurred by the enthusiasm surrounding the "Big Data" paradigm, the mathematical and algorithmic tools of online optimization have found widespread use in problems where the trade-o…
Distributed stochastic optimization via matrix exponential learning
Panayotis Mertikopoulos, E. Veronica Belmega, Romain Negrel +1
In this paper, we investigate a distributed learning scheme for a broad class of stochastic optimization problems and games that arise in signal processing and wireless communicati…