Star-Shaped Denoising Diffusion Probabilistic Models. - Andrey Okhotin; Dmitry Molchanov; Vladimir Arkhipkin; Grigory Bartosh; Viktor Ohanesian; Aibek Alanov; Dmitry P. Vetrov (NeurIPS, 2023)

Star-Shaped Denoising Diffusion Probabilistic Models. - Andrey Okhotin; Dmitry Molchanov; Vladimir Arkhipkin; Grigory Bartosh; Aibek Alanov; Dmitry P. Vetrov (CoRR, 2023)

Neural Diffusion Models. - Grigory Bartosh; Dmitry P. Vetrov; Christian A. Naesseth (ICML, 2024)

Star-Shaped Denoising Diffusion Probabilistic Models (Extended Abstract). - Andrey Okhotin; Dmitry Molchanov; Vladimir Arkhipkin; Grigory Bartosh; Viktor Ohanesian; Aibek Alanov; Dmitry P. Vetrov (KI, 2024)

Neural Flow Diffusion Models: Learnable Forward Process for Improved Diffusion Modelling. - Grigory Bartosh; Dmitry P. Vetrov; Christian Andersson Naesseth (NeurIPS, 2024)

Equivariant Neural Diffusion for Molecule Generation. - François Cornet; Grigory Bartosh; Mikkel N. Schmidt; Christian Andersson Naesseth (NeurIPS, 2024)

Variational Flow Matching for Graph Generation. - Floor Eijkelboom; Grigory Bartosh; Christian Andersson Naesseth; Max Welling; Jan-Willem van de Meent (NeurIPS, 2024)

TEncDM: Understanding the Properties of Diffusion Model in the Space of Language Model Encodings. - Alexander Shabalin; Viacheslav Meshchaninov; Tingir Badmaev; Dmitry Molchanov; Grigory Bartosh; Sergey Markov; Dmitry P. Vetrov (CoRR, 2024)

TEncDM: Understanding the Properties of the Diffusion Model in the Space of Language Model Encodings. - Alexander Shabalin; Viacheslav Meshchaninov; Egor Chimbulatov; Vladislav Lapikov; Roman Kim; Grigory Bartosh; Dmitry Molchanov; Sergey Markov; Dmitry P. Vetrov (AAAI, 2025)

SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equations. - Grigory Bartosh; Dmitry P. Vetrov; Christian A. Naesseth (CoRR, 2025)