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cs.LG2026
Tensor Methods for Language Models: From Token Representation to Training, Adaptation, Inference, Compression, and Interpretability
Matvei Tarasov, Salman Ahmadi-Asl, Andre L. F. de Almeida +1
Large language models (LLMs) are built from structured high-dimensional objects such as token representations, weights, adaptation updates, caches, and activations, whose multiline…
cs.LG2026
Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Parameter Compression of Deep Neural Networks
Andrzej Cichocki, Michal Wietczak
Large deep neural networks are costly to store and deploy because inference must move and evaluate many parameters. This paper studies \emph{Automatically Differentiable Nonlinear…
cs.LG2026
Group Entropies and Mirror Duality: A Class of Flexible Mirror Descent Updates for Machine Learning
Andrzej Cichocki, Piergiulio Tempesta
We introduce a comprehensive theoretical and algorithmic framework that bridges formal group theory and group entropies with modern machine learning, paving the way for an infinite…