5 papers
Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking
Daria Frolova, Talgat Daulbaev, Egor Sevriugov +4
Predicting a protein--ligand binding pose is central to structure-based drug design, yet current docking methods still struggle to combine speed, accuracy and physical plausibility…
Logit-KL Flow Matching: Non-Autoregressive Text Generation via Sampling-Hybrid Inference
Egor Sevriugov, Nikita Dragunov, Anton Razzhigaev +2
Non-autoregressive (NAR) language models offer notable efficiency in text generation by circumventing the sequential bottleneck of autoregressive decoding. However, accurately mode…
Explicit Flow Matching: On The Theory of Flow Matching Algorithms with Applications
Gleb Ryzhakov, Svetlana Pavlova, Egor Sevriugov +1
This paper proposes a novel method, Explicit Flow Matching (ExFM), for training and analyzing flow-based generative models. ExFM leverages a theoretically grounded loss function, E…
Unsupervised evaluation of GAN sample quality: Introducing the TTJac Score
Egor Sevriugov, Ivan Oseledets
Evaluation metrics are essential for assessing the performance of generative models in image synthesis. However, existing metrics often involve high memory and time consumption as…
Robust GAN inversion
Egor Sevriugov, Ivan Oseledets
Recent advancements in real image editing have been attributed to the exploration of Generative Adversarial Networks (GANs) latent space. However, the main challenge of this proced…