Tootfinder

RiemannLoRA: A Unified Riemannian Framework for Ambiguity-Free LoRA Optimization
Vladimir Bogachev, Vladimir Aletov, Alexander Molozhavenko, Denis Bobkov, Vera Soboleva, Aibek Alanov, Maxim Rakhuba
https://arxiv.org/abs/2507.12142

RiemannLoRA: A Unified Riemannian Framework for Ambiguity-Free LoRA Optimization
Low-Rank Adaptation (LoRA) has become a widely adopted standard for parameter-efficient fine-tuning of large language models (LLMs), significantly reducing memory and computational demands. However, challenges remain, including finding optimal initialization strategies or mitigating overparametrization in low-rank matrix factorization. In this work, we propose a novel approach that addresses both of the challenges simultaneously within a unified framework. Our method treats a set of fixed-rank …

Variability-finding in Rubin Data Preview 1 with LSDB
Konstantin Malanchev, Melissa DeLucchi, Neven Caplar, Alex I. Malz, Wilson Beebe, Doug Branton, Sandro Campos, Andrew Connolly, Mi Dai, Jeremy Kubica, Olivia Lynn, Rachel Mandelbaum, Sean McGuire, Eric Aubourg, Robert David Blum, Jeffrey L. Carlin, Francisco Delgado, Emmanuel Gangler, Buell T. Jannuzi, Tim Jenness, Yijung Kang, Arun Kannawadi, Marc Moniez, Andr\'es A. Plazas Malag\'on, Wouter van Reeven, David Sanmartim, Ela…

Variability-finding in Rubin Data Preview 1 with LSDB
The Vera C. Rubin Observatory recently released Data Preview 1 (DP1) in advance of the upcoming Legacy Survey of Space and Time (LSST), which will enable boundless discoveries in time-domain astronomy over the next ten years. DP1 provides an ideal sandbox for validating innovative data analysis approaches for the LSST mission, whose scale challenges established software infrastructure paradigms. This note presents a pair of such pipelines for variability-finding using powerful software infrastr…