233 citations · 1.1k across the 115 of their papers we have counts for
12 papers · 1 filter
Can We Use Neural Regularization to Solve Depth Super-Resolution?
Milena Gazdieva, Oleg Voynov, Alexey Artemov +3
Depth maps captured with commodity sensors often require super-resolution to be used in applications. In this work we study a super-resolution approach based on a variational probl…
Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells
Ildar Abdrakhmanov, Evgenii Kanin, Sergei Boronin +2
We propose a novel approach to data-driven modeling of a transient production of oil wells. We apply the transformer-based neural networks trained on the multivariate time series c…
Generative Modeling with Optimal Transport Maps
Litu Rout, Alexander Korotin, Evgeny Burnaev
With the discovery of Wasserstein GANs, Optimal Transport (OT) has become a powerful tool for large-scale generative modeling tasks. In these tasks, OT cost is typically used as th…
Artificial Text Detection via Examining the Topology of Attention Maps
Laida Kushnareva, Daniil Cherniavskii, Vladislav Mikhailov +6
The impressive capabilities of recent generative models to create texts that are challenging to distinguish from the human-written ones can be misused for generating fake news, pro…
GCN-Denoiser: Mesh Denoising with Graph Convolutional Networks
Yuefan Shen, Hongbo Fu, Zhongshuo Du +5
In this paper, we present GCN-Denoiser, a novel feature-preserving mesh denoising method based on graph convolutional networks (GCNs). Unlike previous learning-based mesh denoising…
A Differentiable Language Model Adversarial Attack on Text Classifiers
Ivan Fursov, Alexey Zaytsev, Pavel Burnyshev +5
Robustness of huge Transformer-based models for natural language processing is an important issue due to their capabilities and wide adoption. One way to understand and improve rob…