no code implementations • 21 Nov 2022 • Jean Pachebat, Sergei Ivanov
Unlike neural networks with millions of trainable parameters, GBDTs optimize loss function in an additive manner and have a single trainable parameter per leaf, which makes it easy to apply high-order optimization of the loss function.
no code implementations • 4 Jul 2022 • Zofia Trstanova, Nadir El Manouzi, Maryline Chen, Andre L. V. da Cunha, Sergei Ivanov
In today's world, the presence of online disinformation and propaganda is more widespread than ever.
no code implementations • 21 Jun 2022 • Gleb Bazhenov, Sergei Ivanov, Maxim Panov, Alexey Zaytsev, Evgeny Burnaev
The problem of out-of-distribution detection for graph classification is far from being solved.
no code implementations • 14 May 2022 • Dmitrii Gavrilev, Nurlybek Amangeldiuly, Sergei Ivanov, Evgeny Burnaev
Prediction of protein-ligand (PL) binding affinity remains the key to drug discovery.
Ranked #2 on Protein-Ligand Affinity Prediction on CSAR-HiQ
1 code implementation • ICLR 2021 • Sergei Ivanov, Liudmila Prokhorenkova
Previous GNN models have mostly focused on networks with homogeneous sparse features and, as we show, are suboptimal in the heterogeneous setting.
no code implementations • 7 Mar 2020 • Nina Mazyavkina, Sergey Sviridov, Sergei Ivanov, Evgeny Burnaev
Many traditional algorithms for solving combinatorial optimization problems involve using hand-crafted heuristics that sequentially construct a solution.
1 code implementation • 26 Oct 2019 • Sergei Ivanov, Sergei Sviridov, Evgeny Burnaev
In recent years there has been a rapid increase in classification methods on graph structured data.