1 citations · 1 across the 10 of their papers we have counts for
4 papers · 1 filter
Gaussian Flow Dynamics: Simulation-Free Neural SDE Learning Beyond One-Time Marginals
Grigory Bartosh, Christian A. Naesseth
Simulation-free training of latent Stochastic Differential Equations (SDEs) relies on a variational posterior process whose one-time marginals are tractable, typically Gaussian. Su…
Forward-Learned Discrete Diffusion: Learning how to noise to denoise faster
Grigory Bartosh, Teodora Pandeva, Sushrut Karmalkar +1
Discrete diffusion models are a powerful class of generative models with strong performance across many domains. For efficiency, however, discrete diffusion typically parameterizes…
Dual-Rate Diffusion: Accelerating diffusion models with an interleaved heavy-light network
Grigory Bartosh, David Ruhe, Emiel Hoogeboom +3
Diffusion models achieve state-of-the-art generative performance but suffer from high computational costs during inference due to the repeated evaluation of a heavy neural network.…
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…