3 papers
cs.CL2026
Towards Latent Diffusion Suitable For Text
Nesta Midavaine, Christian A. Naesseth, Grigory Bartosh
Language diffusion models aim to improve sampling speed and coherence over autoregressive LLMs. We introduce Neural Flow Diffusion Models for language generation, an extension of N…
cs.LG2025
Equivariant Neural Diffusion for Molecule Generation
François Cornet, Grigory Bartosh, Mikkel N. Schmidt +1
We introduce Equivariant Neural Diffusion (END), a novel diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Compared to current state-o…
stat.ML2025
SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations
Grigory Bartosh, Dmitry Vetrov, Christian A. Naesseth
The Latent Stochastic Differential Equation (SDE) is a powerful tool for time series and sequence modeling. However, training Latent SDEs typically relies on adjoint sensitivity me…