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20192026
most citedA mathematical perspective on Transformers

6 citations · 9 across the 14 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.LG2024★ 1 cited

Clustering in Causal Attention Masking

Nikita Karagodin, Yury Polyanskiy, Philippe Rigollet

This work presents a modification of the self-attention dynamics proposed by Geshkovski et al. (arXiv:2312.10794) to better reflect the practically relevant, causally masked attent…

cs.LG2024★ 2 cited

Dynamic metastability in the self-attention model

Borjan Geshkovski, Hugo Koubbi, Yury Polyanskiy +1

We consider the self-attention model - an interacting particle system on the unit sphere, which serves as a toy model for Transformers, the deep neural network architecture behind…

math.PR2024

Entropy Contractions in Markov Chains: Half-Step, Full-Step and Continuous-Time

Pietro Caputo, Zongchen Chen, Yuzhou Gu +1

This paper considers the speed of convergence (mixing) of a finite Markov kernel with respect to the Kullback-Leibler divergence (entropy). Given a Markov kernel one defines ei…

cs.IT2024

Unsourced Multiple Access: A Coding Paradigm for Massive Random Access

Gianluigi Liva, Yury Polyanskiy

This paper is a tutorial introduction to the field of unsourced multiple access (UMAC) protocols. We first provide a historical survey of the evolution of random access protocols,…

cs.IT2024

Evolution of the 5G New Radio Two-Step Random Access towards 6G Unsourced MAC

Patrick Agostini, Jean-Francois Chamberland, Federico Clazzer +7

This report summarizes some considerations on possible evolutions of grant-free random access in the next generation of the 3GPP wireless cellular standard. The analysis is carried…