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cs.LG2024
On Sampling Strategies for Spectral Model Sharding
Denis Korzhenkov, Christos Louizos
The problem of heterogeneous clients in federated learning has recently drawn a lot of attention. Spectral model sharding, i.e., partitioning the model parameters into low-rank mat…
cs.LG2024
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…
cs.LG2018
YASENN: Explaining Neural Networks via Partitioning Activation Sequences
Yaroslav Zharov, Denis Korzhenkov, Pavel Shvechikov +1
We introduce a novel approach to feed-forward neural network interpretation based on partitioning the space of sequences of neuron activations. In line with this approach, we propo…