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08/24/2023
Disentanglement Learning via Topology
We propose TopDis (Topological Disentanglement), a method for learning d...
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06/27/2023
Anomaly Detection in Networks via Score-Based Generative Models
Node outlier detection in attributed graphs is a challenging problem for...
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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...
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06/07/2023
Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts
Rapidly increasing quality of AI-generated content makes it difficult to...
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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...
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04/12/2023
Energy-guided Entropic Neural Optimal Transport
Energy-Based Models (EBMs) are known in the Machine Learning community f...
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03/14/2023
Partial Neural Optimal Transport
We propose a novel neural method to compute partial optimal transport (O...
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03/10/2023
Neural Gromov-Wasserstein Optimal Transport
We present a scalable neural method to solve the Gromov-Wasserstein (GW)...
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03/01/2023
Label Attention Network for sequential multi-label classification
Multi-label classification is a natural problem statement for sequential...
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02/13/2023
Continuous-time convolutions model of event sequences
Massive samples of event sequences data occur in various domains, includ...
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01/31/2023
Learning Topology-Preserving Data Representations
We propose a method for learning topology-preserving data representation...
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01/30/2023
Extremal Domain Translation with Neural Optimal Transport
We propose the extremal transport (ET) which is a mathematical formaliza...
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11/30/2022
Topological Data Analysis for Speech Processing
We apply topological data analysis (TDA) to speech classification proble...
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11/02/2022
Entropic Neural Optimal Transport via Diffusion Processes
We propose a novel neural algorithm for the fundamental problem of compu...
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10/17/2022
Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting
Global warming made the Arctic available for marine operations and creat...
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09/30/2022
Sphere-Guided Training of Neural Implicit Surfaces
In recent years, surface modeling via neural implicit functions has beco...
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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...
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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...
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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...
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06/15/2022
Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport?
Wasserstein Generative Adversarial Networks (WGANs) are the popular gene...
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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...
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05/30/2022
Neural Optimal Transport with General Cost Functionals
We present a novel neural-networks-based algorithm to compute optimal tr...
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05/30/2022
Kernel Neural Optimal Transport
We study the Neural Optimal Transport (NOT) algorithm which uses the gen...
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05/19/2022
Acceptability Judgements via Examining the Topology of Attention Maps
The role of the attention mechanism in encoding linguistic knowledge has...
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03/24/2022
NPBG++: Accelerating Neural Point-Based Graphics
We present a new system (NPBG++) for the novel view synthesis (NVS) task...
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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 ...
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02/02/2022
Unpaired Image Super-Resolution with Optimal Transport Maps
Real-world image super-resolution (SR) tasks often do not have paired da...
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01/28/2022
Wasserstein Iterative Networks for Barycenter Estimation
Wasserstein barycenters have become popular due to their ability to repr...
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01/28/2022
Neural Optimal Transport
We present a novel neural-networks-based algorithm to compute optimal tr...
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12/31/2021
Representation Topology Divergence: A Method for Comparing Neural Network Representations
Comparison of data representations is a complex multi-aspect problem tha...
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12/21/2021
Can We Use Neural Regularization to Solve Depth Super-Resolution?
Depth maps captured with commodity sensors often require super-resolutio...
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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...
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10/06/2021
Generative Modeling with Optimal Transport Maps
With the discovery of Wasserstein GANs, Optimal Transport (OT) has becom...
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09/10/2021
Artificial Text Detection via Examining the Topology of Attention Maps
The impressive capabilities of recent generative models to create texts ...
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08/11/2021
GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks
In this paper, we present GCN-Denoiser, a novel feature-preserving mesh ...
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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...
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07/23/2021
A Differentiable Language Model Adversarial Attack on Text Classifiers
Robustness of huge Transformer-based models for natural language process...
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07/13/2021
3D Parametric Wireframe Extraction Based on Distance Fields
We present a pipeline for parametric wireframe extraction from densely s...
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06/15/2021
Adversarial Attacks on Deep Models for Financial Transaction Records
Machine learning models using transaction records as inputs are popular ...
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06/08/2021
Manifold Topology Divergence: a Framework for Comparing Data Manifolds
We develop a framework for comparing data manifolds, aimed, in particula...
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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...
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06/01/2021
Large-Scale Wasserstein Gradient Flows
Wasserstein gradient flows provide a powerful means of understanding and...
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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 ...
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04/03/2021
COHORTNEY: Deep Clustering for Heterogeneous Event Sequences
There is emerging attention towards working with event sequences. In par...
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02/02/2021
Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization
Wasserstein barycenters provide a geometric notion of the weighted avera...
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01/13/2021
Denoising Score Matching with Random Fourier Features
The density estimation is one of the core problems in statistics. Despit...
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12/31/2020
Topological obstructions in neural networks learning
We apply methods of topological data analysis to loss functions to gain ...
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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...
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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...
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12/03/2020
