output
20052025
most citedLarge-scale magnetic topologies of late M dwarfs

466 citations

Showing cs.LGShow all

16 papers · 1 filter

cs.LG20242 cited

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…

cs.LG2024

An Analysis of Linear Complexity Attention Substitutes with BEST-RQ

Ryan Whetten, Titouan Parcollet, Adel Moumen +2

Self-Supervised Learning (SSL) has proven to be effective in various domains, including speech processing. However, SSL is computationally and memory expensive. This is in part due…

cs.LG2023

Pool-Based Active Learning with Proper Topological Regions

Lies Hadjadj, Emilie Devijver, Remi Molinier +1

Machine learning methods usually rely on large sample size to have good performance, while it is difficult to provide labeled set in many applications. Pool-based active learning m…

cs.LG2023

High Throughput Training of Deep Surrogates from Large Ensemble Runs

Lucas Meyer, Marc Schouler, Robert Alexander Caulk +2

Recent years have seen a surge in deep learning approaches to accelerate numerical solvers, which provide faithful but computationally intensive simulations of the physical world.…

cs.LG20234 cited

Case Studies of Causal Discovery from IT Monitoring Time Series

Ali Aït-Bachir, Charles K. Assaad, Christophe de Bignicourt +5

Information technology (IT) systems are vital for modern businesses, handling data storage, communication, and process automation. Monitoring these systems is crucial for their pro…

cs.LG2023

Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space

Jonatha Anselmi, Bruno Gaujal, Louis-Sébastien Rebuffi

In this paper, we revisit the regret of undiscounted reinforcement learning in MDPs with a birth and death structure. Specifically, we consider a controlled queue with impatient jo…