233 citations · 1.1k across the 115 of their papers we have counts for
11 papers · 2 filters
Light Schrödinger Bridge
Alexander Korotin, Nikita Gushchin, Evgeny Burnaev
Despite the recent advances in the field of computational Schrödinger Bridges (SB), most existing SB solvers are still heavy-weighted and require complex optimization of several ne…
Energy-Guided Continuous Entropic Barycenter Estimation for General Costs
Alexander Kolesov, Petr Mokrov, Igor Udovichenko +5
Optimal transport (OT) barycenters are a mathematically grounded way of averaging probability distributions while capturing their geometric properties. In short, the barycenter tas…
Disentanglement Learning via Topology
Nikita Balabin, Daria Voronkova, Ilya Trofimov +2
We propose TopDis (Topological Disentanglement), a method for learning disentangled representations via adding a multi-scale topological loss term. Disentanglement is a crucial pro…
Anomaly Detection in Networks via Score-Based Generative Models
Dmitrii Gavrilev, Evgeny Burnaev
Node outlier detection in attributed graphs is a challenging problem for which there is no method that would work well across different datasets. Motivated by the state-of-the-art…
Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark
Nikita Gushchin, Alexander Kolesov, Petr Mokrov +4
Over the last several years, there has been significant progress in developing neural solvers for the Schrödinger Bridge (SB) problem and applying them to generative modelling. Thi…
Energy-guided Entropic Neural Optimal Transport
Petr Mokrov, Alexander Korotin, Alexander Kolesov +2
Energy-based models (EBMs) are known in the Machine Learning community for decades. Since the seminal works devoted to EBMs dating back to the noughties, there have been a lot of e…
Light Unbalanced Optimal Transport
Milena Gazdieva, Arip Asadulaev, Alexander Korotin +1
While the continuous Entropic Optimal Transport (EOT) field has been actively developing in recent years, it became evident that the classic EOT problem is prone to different issue…
Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem
Xavier Aramayo Carrasco, Maksim Nekrashevich, Petr Mokrov +2
Recently, the Gromov-Wasserstein Optimal Transport (GWOT) problem has attracted the special attention of the ML community. In this problem, given two distributions supported on two…
Label Attention Network for Temporal Sets Prediction: You Were Looking at a Wrong Self-Attention
Elizaveta Kovtun, Galina Boeva, Andrey Shulga +1
Most user-related data can be represented as a sequence of events associated with a timestamp and a collection of categorical labels. For example, the purchased basket of goods and…
Learning Topology-Preserving Data Representations
Ilya Trofimov, Daniil Cherniavskii, Eduard Tulchinskii +3
We propose a method for learning topology-preserving data representations (dimensionality reduction). The method aims to provide topological similarity between the data manifold an…
Extremal Domain Translation with Neural Optimal Transport
Milena Gazdieva, Alexander Korotin, Daniil Selikhanovych +1
In many unpaired image domain translation problems, e.g., style transfer or super-resolution, it is important to keep the translated image similar to its respective input image. We…