Active Learning with Deep Pre-trained Models for Sequence Tagging of Clinical and Biomedical Texts. - Artem Shelmanov; Vadim Liventsev; Danil Kireev; Nikita Khromov; Alexander Panchenko; Irina Fedulova; Dmitry V. Dylov (BIBM, 2019)

Deep Text Prior: Weakly Supervised Learning for Assertion Classification. - Vadim Liventsev; Irina Fedulova; Dmitry V. Dylov (ICANN (Workshop), 2019)

Neurogenetic programming framework for explainable reinforcement learning. - Vadim Liventsev; Aki Härmä; Milan Petkovic (GECCO Companion, 2021)

BF++: a language for general-purpose neural program synthesis. - Vadim Liventsev; Aki Härmä; Milan Petkovic (CoRR, 2021)

Autoencoders as Tools for Program Synthesis. - Sander de Bruin; Vadim Liventsev; Milan Petkovic (CoRR, 2021)

Towards Effective Patient Simulators. - Vadim Liventsev; Aki Härmä; Milan Petkovic (Frontiers Artif. Intell., 2021)

Fully Autonomous Programming with Large Language Models. - Vadim Liventsev; Anastasiia Grishina; Aki Härmä; Leon Moonen (GECCO, 2023)

Intensive Care as One Big Sequence Modeling Problem. - Vadim Liventsev; Tobias Fritz (CoRR, 2024)

PhilHumans: Benchmarking Machine Learning for Personal Health. - Vadim Liventsev; Vivek Kumar 007; Allmin Pradhap Singh Susaiyah; Zixiu Wu; Ivan Rodin; Asfand Yaar; Simone Balloccu; Marharyta Beraziuk; Sebastiano Battiato; Giovanni Maria Farinella; Aki Härmä; Rim Helaoui; et al. (CoRR, 2024)

Tree Variational Autoencoder for Code. - Vadim Liventsev; Sander de Bruin; Aki Härmä; Milan Petkovic (IEEE Access, 2025)

Fully Autonomous Programming Using Iterative Multi-Agent Debugging with Large Language Models. - Anastasiia Grishina; Vadim Liventsev; Aki Härmä; Leon Moonen (ACM Trans. Evol. Learn. Optim., 2025)