3 papers
cs.LG2026
SoftMoE: Soft Differentiable Routing for Mixture-of-Experts in LLMs
Mikołaj Zasada, Łukasz Struski, Jacek Tabor +1
Sparse Mixture-of-Experts (MoE) architectures enable scaling LLM parameters under a fixed inference budget by activating only a small subset of experts via top- routing. While t…
cs.GT2026
Learning Real-Life Approval Elections
Piotr Faliszewski, Łukasz Janeczko, Andrzej Kaczmarczyk +3
We study the independent approval model (IAM) for approval elections, where each candidate has its own approval probability and is approved independently of the other ones. This mo…
cs.LG2022
Neural Representations Reveal Distinct Modes of Class Fitting in Residual Convolutional Networks
Michał Jamroż, Marcin Kurdziel
We leverage probabilistic models of neural representations to investigate how residual networks fit classes. To this end, we estimate class-conditional density models for represent…