28 citations · 61 across the 14 of their papers we have counts for
17 papers
Representation-Space MMD for Diffusion Language Models
Ilya Drobyshevskiy, Ilia Sudakov, Maksim Semenov +7
We introduce a post-training method for diffusion language models (DLMs) that minimizes Maximum Mean Discrepancy (MMD) between generated and reference distributions in the feature…
Registers Matter for Pixel-Space Diffusion Transformers
Nikita Starodubcev, Ilia Sudakov, Ilya Drobyshevskiy +2
Vision Transformers (ViTs) are known to exhibit high-norm patch-token outliers that degrade feature map quality, a problem effectively mitigated by register tokens. As diffusion mo…
Revisiting Autoregressive Models for Generative Image Classification
Ilia Sudakov, Artem Babenko, Dmitry Baranchuk
Class-conditional generative models have emerged as accurate and robust classifiers, with diffusion models demonstrating clear advantages over other visual generative paradigms, in…
Rethinking Global Text Conditioning in Diffusion Transformers
Nikita Starodubcev, Daniil Pakhomov, Zongze Wu +6
Diffusion transformers typically incorporate textual information via attention layers and a modulation mechanism using a pooled text embedding. Nevertheless, recent approaches disc…
Scale-wise Distillation of Diffusion Models
Nikita Starodubcev, Ilya Drobyshevskiy, Denis Kuznedelev +2
Recent diffusion distillation methods have achieved remarkable progress, enabling high-quality -step sampling for large-scale text-conditional image and video diffusion mo…
Inverse Entropic Optimal Transport Solves Semi-supervised Learning via Data Likelihood Maximization
Mikhail Persiianov, Arip Asadulaev, Nikita Andreev +5
Learning conditional distributions is a central problem in machine learning, which is typically approached via supervised methods with paired data .…