7 citations · 8 across the 9 of their papers we have counts for
3 papers · 1 filter
Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling
Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth
Conventional diffusion models typically relies on a fixed forward process, which implicitly defines complex marginal distributions over latent variables. This can often complicate…
Diffusion on language model encodings for protein sequence generation
Viacheslav Meshchaninov, Pavel Strashnov, Andrey Shevtsov +4
Protein sequence design has seen significant advances through discrete diffusion and autoregressive approaches, yet the potential of continuous diffusion remains underexplored. Her…
TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings
Alexander Shabalin, Viacheslav Meshchaninov, Egor Chimbulatov +6
This paper presents the Text Encoding Diffusion Model (TEncDM), a novel approach to diffusion modeling that operates in the space of pre-trained language model encodings. In contra…