10 citations · 24 across the 4 of their papers we have counts for
13 papers
A Mutual Information Perspective on Federated Contrastive Learning
Christos Louizos, Matthias Reisser, Denis Korzhenkov
We investigate contrastive learning in the federated setting through the lens of SimCLR and multi-view mutual information maximization. In doing so, we uncover a connection between…
Federated Mixture of Experts
Matthias Reisser, Christos Louizos, Efstratios Gavves +1
Federated learning (FL) has emerged as the predominant approach for collaborative training of neural network models across multiple users, without the need to gather the data at a…
Federated Learning of User Verification Models Without Sharing Embeddings
Hossein Hosseini, Hyunsin Park, Sungrack Yun +3
We consider the problem of training User Verification (UV) models in federated setting, where each user has access to the data of only one class and user embeddings cannot be share…
Federated Learning of User Authentication Models
Hossein Hosseini, Sungrack Yun, Hyunsin Park +3
Machine learning-based User Authentication (UA) models have been widely deployed in smart devices. UA models are trained to map input data of different users to highly separable em…
Bayesian Bits: Unifying Quantization and Pruning
Mart van Baalen, Christos Louizos, Markus Nagel +4
We introduce Bayesian Bits, a practical method for joint mixed precision quantization and pruning through gradient based optimization. Bayesian Bits employs a novel decomposition o…
Up or Down? Adaptive Rounding for Post-Training Quantization
Markus Nagel, Rana Ali Amjad, Mart van Baalen +2
When quantizing neural networks, assigning each floating-point weight to its nearest fixed-point value is the predominant approach. We find that, perhaps surprisingly, this is not…