Search Results for author: Sergey Nikolenko

Found 30 papers, 12 papers with code

ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering

1 code implementation31 May 2024 Ilya Shenbin, Sergey Nikolenko

We present ImplicitSLIM, a novel unsupervised learning approach for sparse high-dimensional data, with applications to collaborative filtering.

Collaborative Filtering

GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding

1 code implementation14 Nov 2023 Konstantin Yakovlev, Alexander Podolskiy, Andrey Bout, Sergey Nikolenko, Irina Piontkovskaya

Grammatical error correction (GEC) is an important NLP task that is currently usually solved with autoregressive sequence-to-sequence models.

Decoder Denoising +2

AI-generated text boundary detection with RoFT

no code implementations14 Nov 2023 Laida Kushnareva, Tatiana Gaintseva, German Magai, Serguei Barannikov, Dmitry Abulkhanov, Kristian Kuznetsov, Eduard Tulchinskii, Irina Piontkovskaya, Sergey Nikolenko

Due to the rapid development of large language models, people increasingly often encounter texts that may start as written by a human but continue as machine-generated.

Boundary Detection Text Detection +2

Early Warning Prediction with Automatic Labeling in Epilepsy Patients

no code implementations9 Oct 2023 Peng Zhang, Ting Gao, Jin Guo, Jinqiao Duan, Sergey Nikolenko

Early warning for epilepsy patients is crucial for their safety and well-being, in particular to prevent or minimize the severity of seizures.

EEG Meta-Learning

Machine Learning for SAT: Restricted Heuristics and New Graph Representations

no code implementations18 Jul 2023 Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev, Sergey Nikolenko

Boolean satisfiability (SAT) is a fundamental NP-complete problem with many applications, including automated planning and scheduling.

Scheduling

Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts

1 code implementation NeurIPS 2023 Eduard Tulchinskii, Kristian Kuznetsov, Laida Kushnareva, Daniil Cherniavskii, Serguei Barannikov, Irina Piontkovskaya, Sergey Nikolenko, Evgeny Burnaev

Rapidly increasing quality of AI-generated content makes it difficult to distinguish between human and AI-generated texts, which may lead to undesirable consequences for society.

CCT-Code: Cross-Consistency Training for Multilingual Clone Detection and Code Search

no code implementations19 May 2023 Nikita Sorokin, Dmitry Abulkhanov, Sergey Nikolenko, Valentin Malykh

We consider the clone detection and information retrieval problems for source code, well-known tasks important for any programming language.

Clone Detection Code Search +2

STIR: Siamese Transformer for Image Retrieval Postprocessing

1 code implementation26 Apr 2023 Aleksei Shabanov, Aleksei Tarasov, Sergey Nikolenko

Current metric learning approaches for image retrieval are usually based on learning a space of informative latent representations where simple approaches such as the cosine distance will work well.

Image Retrieval Metric Learning +3

Topological Data Analysis for Speech Processing

no code implementations30 Nov 2022 Eduard Tulchinskii, Kristian Kuznetsov, Laida Kushnareva, Daniil Cherniavskii, Serguei Barannikov, Irina Piontkovskaya, Sergey Nikolenko, Evgeny Burnaev

We apply topological data analysis (TDA) to speech classification problems and to the introspection of a pretrained speech model, HuBERT.

Topological Data Analysis

Personality-Driven Social Multimedia Content Recommendation

no code implementations25 Jul 2022 Qi Yang, Sergey Nikolenko, Alfred Huang, Aleksandr Farseev

In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale.

Marketing Recommendation Systems

DetIE: Multilingual Open Information Extraction Inspired by Object Detection

1 code implementation24 Jun 2022 Michael Vasilkovsky, Anton Alekseev, Valentin Malykh, Ilya Shenbin, Elena Tutubalina, Dmitriy Salikhov, Mikhail Stepnov, Andrey Chertok, Sergey Nikolenko

Our model sets the new state of the art performance of 67. 7% F1 on CaRB evaluated as OIE2016 while being 3. 35x faster at inference than previous state of the art.

Multilingual NLP Object +2

Near-Zero-Shot Suggestion Mining with a Little Help from WordNet

no code implementations25 Nov 2021 Anton Alekseev, Elena Tutubalina, Sejeong Kwon, Sergey Nikolenko

In this work, we explore the constructive side of online reviews: advice, tips, requests, and suggestions that users provide about goods, venues, services, and other items of interest.

Suggestion mining

Towards General Purpose Geometry-Preserving Single-View Depth Estimation

1 code implementation25 Sep 2020 Mikhail Romanov, Nikolay Patatkin, Anna Vorontsova, Sergey Nikolenko, Anton Konushin, Dmitry Senyushkin

Our work shows that a model trained on this data along with conventional datasets can gain accuracy while predicting correct scene geometry.

Monocular Depth Estimation Scene Understanding

Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification

no code implementations17 Jun 2020 Anton Alekseev, Elena Tutubalina, Valentin Malykh, Sergey Nikolenko

Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling.

Aspect Extraction domain classification +2

CommentsRadar: Dive into Unique Data on All Comments on the Web

no code implementations16 Aug 2019 Sergey Nikolenko, Elena Tutubalina, Zulfat Miftahutdinov, Eugene Beloded

We introduce an entity-centric search engineCommentsRadarthatpairs entity queries with articles and user opinions covering a widerange of topics from top commented sites.

Free-Lunch Saliency via Attention in Atari Agents

1 code implementation7 Aug 2019 Dmitry Nikulin, Anastasia Ianina, Vladimir Aliev, Sergey Nikolenko

We show experimentally that a network with an FLS module exhibits performance similar to the baseline (i. e., it is "free", with no performance cost) and can be used as a drop-in replacement for reinforcement learning agents.

Decision Making reinforcement-learning +1

AspeRa: Aspect-Based Rating Prediction Based on User Reviews

no code implementations WS 2019 Elena Tutubalina, Valentin Malykh, Sergey Nikolenko, Anton Alekseev, Ilya Shenbin

We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items.

Aspect Extraction

Large-Scale Transfer Learning for Natural Language Generation

1 code implementation ACL 2019 Sergey Golovanov, Rauf Kurbanov, Sergey Nikolenko, Kyryl Truskovskyi, Alex Tselousov, er, Thomas Wolf

Large-scale pretrained language models define state of the art in natural language processing, achieving outstanding performance on a variety of tasks.

Open-Domain Dialog Text Generation +1

Sequence Learning with RNNs for Medical Concept Normalization in User-Generated Texts

no code implementations28 Nov 2018 Elena Tutubalina, Zulfat Miftahutdinov, Sergey Nikolenko, Valentin Malykh

In this work, we consider the medical concept normalization problem, i. e., the problem of mapping a disease mention in free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS).

Medical Concept Normalization Semantic Similarity +1

Cannot find the paper you are looking for? You can Submit a new open access paper.