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I'll be at NeurIPS next week, presenting our work "A Generative Model of Symmetry Transformations." In it, we propose a symmetry-aware generative model that discovers which (approximate) symmetries are present in a dataset, and can be leveraged to improve data efficiency. 🧵⬇️

Atinary @ #NeurIPS in Vancouver this week🍁 Connect with our #AI #ML researchers Víctor Sabanza Gil & Shreyas Padhy. Our research paper on Multi-Fidelity Bayesian Optimization (MFBO) will be @ AIDrugX workshop on Dec 15! Full article: arxiv.org/abs/2410.00544 Atinary Technologies Inc. #SDLabs

We're excited to be at #NeurIPS Vancouver! See the papers we'll be presenting at the main conference below:

Come chat with folks from our group!

Tweeting again about sampling, my favourite 2024 Monte Carlo paper is arxiv.org/abs/2307.01050 by F. Vargas, Shreyas Padhy, D. Blessing & N. Nüsken: . Propose a "simple" loss to learn the drift you need to add to Langevin to follow a fixed probability path.

Thanks for the kind words Arnaud Doucet ! I wanted to shout-out some other great work in the same vein as us - arxiv.org/abs/2501.06148 (Julius Berner @ICLR‘25, Lorenz Richter, Marcin Sendera et al) arxiv.org/abs/2410.02711 (Michael Albergo @ICLR2025 et al) arxiv.org/abs/2412.07081 (Junhua Chen et al)

Checkout this paper with some really interesting insights led by the excellent Jiajun He and Yuanqi Du - TLDR: Neural density samplers really need guidance imposed through Langevin annealing to make them work well