184 citations · 196 across the 11 of their papers we have counts for
11 papers
LAMeD: LLM-generated Annotations for Memory Leak Detection
Ekaterina Shemetova, Ilya Shenbin, Ivan Smirnov +5
Static analysis tools are widely used to detect software bugs and vulnerabilities but often struggle with scalability and efficiency in complex codebases. Traditional approaches re…
Syntactic Transfer to Kyrgyz Using the Treebank Translation Method
Anton Alekseev, Alina Tillabaeva, Gulnara Dzh. Kabaeva +1
The Kyrgyz language, as a low-resource language, requires significant effort to create high-quality syntactic corpora. This study proposes an approach to simplify the development p…
Neural Click Models for Recommender Systems
Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev +4
We develop and evaluate neural architectures to model the user behavior in recommender systems (RS) inspired by click models for Web search but going beyond standard click models.…
DFT: A Universal Quantum Chemistry Dataset of Drug-Like Molecules and a Benchmark for Neural Network Potentials
Kuzma Khrabrov, Anton Ber, Artem Tsypin +10
Methods of computational quantum chemistry provide accurate approximations of molecular properties crucial for computer-aided drug discovery and other areas of chemical science. Ho…
Benchmarking Multilabel Topic Classification in the Kyrgyz Language
Anton Alekseev, Sergey I. Nikolenko, Gulnara Kabaeva
Kyrgyz is a very underrepresented language in terms of modern natural language processing resources. In this work, we present a new public benchmark for topic classification in Kyr…
Machine Learning for SAT: Restricted Heuristics and New Graph Representations
Mikhail Shirokikh, Ilya Shenbin, Anton Alekseev +1
Boolean satisfiability (SAT) is a fundamental NP-complete problem with many applications, including automated planning and scheduling. To solve large instances, SAT solvers have to…
DetIE: Multilingual Open Information Extraction Inspired by Object Detection
Michael Vasilkovsky, Anton Alekseev, Valentin Malykh +6
State of the art neural methods for open information extraction (OpenIE) usually extract triplets (or tuples) iteratively in an autoregressive or predicate-based manner in order no…
Near-Zero-Shot Suggestion Mining with a Little Help from WordNet
Anton Alekseev, Elena Tutubalina, Sejeong Kwon +1
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 inter…
Improving unsupervised neural aspect extraction for online discussions using out-of-domain classification
Anton Alekseev, Elena Tutubalina, Valentin Malykh +1
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.…
RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
Ilya Shenbin, Anton Alekseev, Elena Tutubalina +2
Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering. In particular, the recently proposed Mult-VAE model, which…
AspeRa: Aspect-based Rating Prediction Model
Sergey I. Nikolenko, Elena Tutubalina, Valentin Malykh +2
We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent a…