Philipp Tuchel

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20242026

most citedA Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption

8 citations · 8 across the 8 of their papers we have counts for

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math.PR2024

Limit Theorems for the Volume of Random Projections and Sections of $ℓ_{p}^{N}$ -balls

Joscha Prochno, Christoph Thaele, Philipp Tuchel

Let $B_{p}^{N}$ be the $N$ -dimensional unit ball corresponding to the $ℓ_{p}$ -norm. For each $N \in N$ we sample a uniform random subspace $E_{N}$ of fixed dimension $m\i…

cs.LG2024★ 8 cited

A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption

Marcus Rüb, Philipp Tuchel, Axel Sikora +1

A new algorithm for incremental learning in the context of Tiny Machine learning (TinyML) is presented, which is optimized for low-performance and energy efficient embedded devices…

Limit Theorems for the Volume of Random Projections and Sections of ℓpN​-balls

A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption

Limit Theorems for the Volume of Random Projections and Sections of $ℓ_{p}^{N}$ -balls