184 citations · 190 across the 8 of their papers we have counts for
8 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…
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…
ImplicitSLIM and How it Improves Embedding-based Collaborative Filtering
Ilya Shenbin, Sergey Nikolenko
We present ImplicitSLIM, a novel unsupervised learning approach for sparse high-dimensional data, with applications to collaborative filtering. Sparse linear methods (SLIM) and the…
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…