58 citations · 78 across the 15 of their papers we have counts for
8 papers · 1 filter
Plan, Don't Pose: Long Composite Motion Generation with Text-Aligned BFM
Nikolay Shvetsov, Maksim Bobrin, Nazar Buzun +2
Text-to-motion (T2M) generation has broad applications in character animation, virtual avatars, and human-robot interaction. Existing methods typically generate pose trajectories o…
Unlocking the Duality between Flow and Field Matching
Daniil Shlenskii, Alexander Varlamov, Nazar Buzun +1
Conditional Flow Matching (CFM) unifies conventional generative paradigms such as diffusion models and flow matching. Interaction Field Matching (IFM) is a newer framework that gen…
Q-RAG: Long Context Multi-step Retrieval via Value-based Embedder Training
Artyom Sorokin, Nazar Buzun, Alexander Anokhin +7
Retrieval-Augmented Generation (RAG) methods enhance LLM performance by efficiently filtering relevant context for LLMs, reducing hallucinations and inference cost. However, most e…
HOTA: Hamiltonian framework for Optimal Transport Advection
Nazar Buzun, Daniil Shlenskii, Maxim Bobrin +1
Optimal transport (OT) has become a natural framework for guiding the probability flows. Yet, the majority of recent generative models assume trivial geometry (e.g., Euclidean) and…
ENOT: Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport
Nazar Buzun, Maksim Bobrin, Dmitry V. Dylov
We present a new approach for Neural Optimal Transport (NOT) training procedure, capable of accurately and efficiently estimating optimal transportation plan via specific regulariz…
Align Your Intents: Offline Imitation Learning via Optimal Transport
Maksim Bobrin, Nazar Buzun, Dmitrii Krylov +1
Offline Reinforcement Learning (RL) addresses the problem of sequential decision-making by learning optimal policy through pre-collected data, without interacting with the environm…
Explain My Surprise: Learning Efficient Long-Term Memory by Predicting Uncertain Outcomes
Artyom Sorokin, Nazar Buzun, Leonid Pugachev +1
In many sequential tasks, a model needs to remember relevant events from the distant past to make correct predictions. Unfortunately, a straightforward application of gradient base…
Unsupervised non-parametric change point detection in quasi-periodic signals
Nikolay Shvetsov, Nazar Buzun, Dmitry V. Dylov
We propose a new unsupervised and non-parametric method to detect change points in intricate quasi-periodic signals. The detection relies on optimal transport theory combined with…