Evgeny Burnaev

research

∙ 08/24/2023

Disentanglement Learning via Topology

We propose TopDis (Topological Disentanglement), a method for learning d...

0 Nikita Balabin, et al. ∙

research

∙ 06/27/2023

Anomaly Detection in Networks via Score-Based Generative Models

Node outlier detection in attributed graphs is a challenging problem for...

0 Dmitrii Gavrilev, et al. ∙

research

∙ 06/16/2023

Building the Bridge of Schrödinger: A Continuous Entropic Optimal Transport Benchmark

Over the last several years, there has been a significant progress in de...

6 Nikita Gushchin, et al. ∙

research

∙ 06/07/2023

Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts

Rapidly increasing quality of AI-generated content makes it difficult to...

1 Eduard Tulchinskii, et al. ∙

research

∙ 05/29/2023

Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects

We develop a method that recovers the surface, materials, and illuminati...

6 Yue Fan, et al. ∙

research

∙ 04/12/2023

Energy-guided Entropic Neural Optimal Transport

Energy-Based Models (EBMs) are known in the Machine Learning community f...

9 Petr Mokrov, et al. ∙

research

∙ 03/14/2023

Partial Neural Optimal Transport

We propose a novel neural method to compute partial optimal transport (O...

5 Milena Gazdieva, et al. ∙

research

∙ 03/10/2023

Neural Gromov-Wasserstein Optimal Transport

We present a scalable neural method to solve the Gromov-Wasserstein (GW)...

4 Maksim Nekrashevich, et al. ∙

research

∙ 03/01/2023

Label Attention Network for sequential multi-label classification

Multi-label classification is a natural problem statement for sequential...

1 Elizaveta Kovtun, et al. ∙

research

∙ 02/13/2023

Continuous-time convolutions model of event sequences

Massive samples of event sequences data occur in various domains, includ...

6 Vladislav Zhuzhel, et al. ∙

research

∙ 01/31/2023

Learning Topology-Preserving Data Representations

We propose a method for learning topology-preserving data representation...

11 Ilya Trofimov, et al. ∙

research

∙ 01/30/2023

Extremal Domain Translation with Neural Optimal Transport

We propose the extremal transport (ET) which is a mathematical formaliza...

2 Milena Gazdieva, et al. ∙

research

∙ 11/30/2022

Topological Data Analysis for Speech Processing

We apply topological data analysis (TDA) to speech classification proble...

0 Eduard Tulchinskii, et al. ∙

research

∙ 11/02/2022

Entropic Neural Optimal Transport via Diffusion Processes

We propose a novel neural algorithm for the fundamental problem of compu...

6 Nikita Gushchin, et al. ∙

research

∙ 10/17/2022

Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting

Global warming made the Arctic available for marine operations and creat...

0 Timofey Grigoryev, et al. ∙

research

∙ 09/30/2022

Sphere-Guided Training of Neural Implicit Surfaces

In recent years, surface modeling via neural implicit functions has beco...

13 Andreea Dogaru, et al. ∙

research

∙ 09/07/2022

Multi-NeuS: 3D Head Portraits from Single Image with Neural Implicit Functions

We present an approach for the reconstruction of textured 3D meshes of h...

0 Egor Burkov, et al. ∙

research

∙ 08/31/2022

QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise

There is a constant need for high-performing and computationally efficie...

0 Egor Shvetsov, et al. ∙

research

∙ 06/27/2022

Transfer learning for ensembles: reducing computation time and keeping the diversity

Transferring a deep neural network trained on one problem to another req...

0 Ilya Shashkov, et al. ∙

research

∙ 06/15/2022

Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?

Wasserstein Generative Adversarial Networks (WGANs) are the popular gene...

4 Alexander Korotin, et al. ∙

research

∙ 06/06/2022

Scan2Part: Fine-grained and Hierarchical Part-level Understanding of Real-World 3D Scans

We propose Scan2Part, a method to segment individual parts of objects in...

8 Alexandr Notchenko, et al. ∙

research

∙ 05/30/2022

Neural Optimal Transport with General Cost Functionals

We present a novel neural-networks-based algorithm to compute optimal tr...

0 Arip Asadulaev, et al. ∙

research

∙ 05/30/2022

Kernel Neural Optimal Transport

We study the Neural Optimal Transport (NOT) algorithm which uses the gen...

0 Alexander Korotin, et al. ∙

research

∙ 05/19/2022

Acceptability Judgements via Examining the Topology of Attention Maps

The role of the attention mechanism in encoding linguistic knowledge has...

0 Daniil Cherniavskii, et al. ∙

research

∙ 03/24/2022

NPBG++: Accelerating Neural Point-Based Graphics

We present a new system (NPBG++) for the novel view synthesis (NVS) task...

0 Ruslan Rakhimov, et al. ∙

research

∙ 03/11/2022

Multi-sensor large-scale dataset for multi-view 3D reconstruction

We present a new multi-sensor dataset for 3D surface reconstruction. It ...

0 Oleg Voynov, et al. ∙

research

∙ 02/02/2022

Unpaired Image Super-Resolution with Optimal Transport Maps

Real-world image super-resolution (SR) tasks often do not have paired da...

0 Milena Gazdieva, et al. ∙

research

∙ 01/28/2022

Wasserstein Iterative Networks for Barycenter Estimation

Wasserstein barycenters have become popular due to their ability to repr...

0 Alexander Korotin, et al. ∙

research

∙ 01/28/2022

Neural Optimal Transport

We present a novel neural-networks-based algorithm to compute optimal tr...

0 Alexander Korotin, et al. ∙

research

∙ 12/31/2021

Representation Topology Divergence: A Method for Comparing Neural Network Representations

Comparison of data representations is a complex multi-aspect problem tha...

0 Serguei Barannikov, et al. ∙

research

∙ 12/21/2021

Can We Use Neural Regularization to Solve Depth Super-Resolution?

Depth maps captured with commodity sensors often require super-resolutio...

9 Milena Gazdieva, et al. ∙

research

∙ 10/12/2021

Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells

We propose a novel approach to data-driven modeling of a transient produ...

0 Ildar Abdrakhmanov, et al. ∙

research

∙ 10/06/2021

Generative Modeling with Optimal Transport Maps

With the discovery of Wasserstein GANs, Optimal Transport (OT) has becom...

0 Litu Rout, et al. ∙

research

∙ 09/10/2021

Artificial Text Detection via Examining the Topology of Attention Maps

The impressive capabilities of recent generative models to create texts ...

0 Laida Kushnareva, et al. ∙

research

∙ 08/11/2021

GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks

In this paper, we present GCN-Denoiser, a novel feature-preserving mesh ...

0 Yuefan Shen, et al. ∙

research

∙ 08/02/2021

Data-driven model for hydraulic fracturing design optimization. Part II: Inverse problem

We describe a stacked model for predicting the cumulative fluid producti...

0 Viktor Duplyakov, et al. ∙

research

∙ 07/23/2021

A Differentiable Language Model Adversarial Attack on Text Classifiers

Robustness of huge Transformer-based models for natural language process...

0 Ivan Fursov, et al. ∙

research

∙ 07/13/2021

3D Parametric Wireframe Extraction Based on Distance Fields

We present a pipeline for parametric wireframe extraction from densely s...

0 Albert Matveev, et al. ∙

research

∙ 06/15/2021

Adversarial Attacks on Deep Models for Financial Transaction Records

Machine learning models using transaction records as inputs are popular ...

0 Ivan Fursov, et al. ∙

research

∙ 06/08/2021

Manifold Topology Divergence: a Framework for Comparing Data Manifolds

We develop a framework for comparing data manifolds, aimed, in particula...

0 Serguei Barannikov, et al. ∙

research

∙ 06/03/2021

Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark

Despite the recent popularity of neural network-based solvers for optima...

0 Alexander Korotin, et al. ∙

research

∙ 06/01/2021

Large-Scale Wasserstein Gradient Flows

Wasserstein gradient flows provide a powerful means of understanding and...

0 Petr Mokrov, et al. ∙

research

∙ 05/25/2021

Towards Unpaired Depth Enhancement and Super-Resolution in the Wild

Depth maps captured with commodity sensors are often of low quality and ...

0 Aleksandr Safin, et al. ∙

research

∙ 04/03/2021

COHORTNEY: Deep Clustering for Heterogeneous Event Sequences

There is emerging attention towards working with event sequences. In par...

0 Vladislav Zhuzhel, et al. ∙

research

∙ 02/02/2021

Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization

Wasserstein barycenters provide a geometric notion of the weighted avera...

0 Alexander Korotin, et al. ∙

research

∙ 01/13/2021

Denoising Score Matching with Random Fourier Features

The density estimation is one of the core problems in statistics. Despit...

2 Tsimboy Olga, et al. ∙

research

∙ 12/31/2020

Topological obstructions in neural networks learning

We apply methods of topological data analysis to loss functions to gain ...

0 Serguei Barannikov, et al. ∙

research

∙ 12/17/2020

Relightable 3D Head Portraits from a Smartphone Video

In this work, a system for creating a relightable 3D portrait of a human...

0 Artem Sevastopolsky, et al. ∙

research

∙ 12/07/2020

AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors

The emerging progress of eSports lacks the tools for ensuring high-quali...

0 Anton Smerdov, et al. ∙

research

∙ 12/03/2020

Towards Part-Based Understanding of RGB-D Scans

Recent advances in 3D semantic scene understanding have shown impressive...

0 Alexey Bokhovkin, et al. ∙

Evgeny Burnaev

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