18 citations · 27 across the 8 of their papers we have counts for
8 papers
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…
Neural Optimal Transport with General Cost Functionals
Arip Asadulaev, Alexander Korotin, Vage Egiazarian +2
We introduce a novel neural network-based algorithm to compute optimal transport (OT) plans for general cost functionals. In contrast to common Euclidean costs, i.e., or $…
Loss Barcode: A Topological Measure of Escapability in Loss Landscapes
Serguei Barannikov, Daria Voronkova, Alexander Mironenko +4
Neural network training is commonly based on SGD. However, the understanding of SGD's ability to converge to good local minima, given the non-convex nature of loss functions and th…
DEF: Deep Estimation of Sharp Geometric Features in 3D Shapes
Albert Matveev, Ruslan Rakhimov, Alexey Artemov +6
We propose Deep Estimators of Features (DEFs), a learning-based framework for predicting sharp geometric features in sampled 3D shapes. Differently from existing data-driven method…
Geometric Attention for Prediction of Differential Properties in 3D Point Clouds
Albert Matveev, Alexey Artemov, Denis Zorin +1
Estimation of differential geometric quantities in discrete 3D data representations is one of the crucial steps in the geometry processing pipeline. Specifically, estimating normal…