3 papers
cs.LG2026
Cascaded Transfer: Learning Many Tasks under Budget Constraints
Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude +2
In distributed applications, such as energy demand forecasting at the substation level or federated learning, a large number of related tasks must be learned by different models, w…
cs.LG2025
Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application
Gonzalo Iñaki Quintana, Laurence Vancamberg, Vincent Jugnon +2
This work studies the relationship between Contrastive Learning and Domain Adaptation from a theoretical perspective. The two standard contrastive losses, NT-Xent loss (Self-superv…
cs.LG2024
BN-SCAFFOLD: controlling the drift of Batch Normalization statistics in Federated Learning
Gonzalo Iñaki Quintana, Laurence Vancamberg, Vincent Jugnon +2
Federated Learning (FL) is gaining traction as a learning paradigm for training Machine Learning (ML) models in a decentralized way. Batch Normalization (BN) is ubiquitous in Deep…