output
20192026
most citedNeural Entity Linking: A Survey of Models Based on Deep Learning

144 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

11 papers

cs.CV2026

AOV-GS: Active Open-Vocabulary Semantic Mapping with 3D Gaussian Splatting

Rodion Shkokov, Tatiana Zemskova, Dmitry Yudin

Robots deployed in human-centered environments need maps that support both geometric navigation and open-vocabulary semantic queries beyond a fixed label set. A key challenge is en…

cs.CL2026

Overview of BioASQ 2026: The fourteenth BioASQ Challenge on Large-Scale Biomedical Semantic Indexing and Question Answering

Anastasios Nentidis, Georgios Katsimpras, Anastasia Krithara +18

This paper presents an overview of the fourteenth edition of the BioASQ challenge, organized in the context of the Conference and Labs of the Evaluation Forum (CLEF) 2026. BioASQ i…

cs.LG2025

RTD-Lite: Scalable Topological Analysis for Comparing Weighted Graphs in Learning Tasks

Eduard Tulchinskii, Daria Voronkova, Ilya Trofimov +2

Topological methods for comparing weighted graphs are valuable in various learning tasks but often suffer from computational inefficiency on large datasets. We introduce RTD-Lite,…

cs.CV2024

Scalar Function Topology Divergence: Comparing Topology of 3D Objects

Ilya Trofimov, Daria Voronkova, Eduard Tulchinskii +2

We propose a new topological tool for computer vision - Scalar Function Topology Divergence (SFTD), which measures the dissimilarity of multi-scale topology between sublevel sets o…

cs.LG2023★ 1 cited

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…

cs.LG2023★ 2 cited

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…

cs.SD2022★ 43 cited

HiFi++: a Unified Framework for Bandwidth Extension and Speech Enhancement

Pavel Andreev, Aibek Alanov, Oleg Ivanov +1

Generative adversarial networks have recently demonstrated outstanding performance in neural vocoding outperforming best autoregressive and flow-based models. In this paper, we sho…

cs.LG2021★ 4 cited

Manifold Topology Divergence: a Framework for Comparing Data Manifolds

Serguei Barannikov, Ilya Trofimov, Grigorii Sotnikov +4

We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models. We describe a novel tool, Cross-Barcode(P,Q), that, giv…

cs.CL2020★ 144 cited

Neural Entity Linking: A Survey of Models Based on Deep Learning

Ozge Sevgili, Artem Shelmanov, Mikhail Arkhipov +2

This survey presents a comprehensive description of recent neural entity linking (EL) systems developed since 2015 as a result of the "deep learning revolution" in natural language…

cs.LG2020★ 27 cited

CoLES: Contrastive Learning for Event Sequences with Self-Supervision

Dmitrii Babaev, Ivan Kireev, Nikita Ovsov +4

We address the problem of self-supervised learning on discrete event sequences generated by real-world users. Self-supervised learning incorporates complex information from the raw…

cs.LG2019

Barcodes as Summary of Loss Function Topology

Serguei Barannikov, Alexander Korotin, Dmitry Oganesyan +2

We propose to study neural networks' loss surfaces by methods of topological data analysis. We suggest to apply barcodes of Morse complexes to explore topology of loss surfaces. An…