Active learning with deep pre-trained models for sequence tagging of clinical and biomedical texts A Shelmanov, V Liventsev, D Kireev, N Khromov, A Panchenko, ... 2019 IEEE international conference on bioinformatics and biomedicine (BIBM …, 2019 | 35 | 2019 |
Fully autonomous programming with large language models V Liventsev, A Grishina, A Härmä, L Moonen Proceedings of the Genetic and Evolutionary Computation Conference, 1146-1155, 2023 | 31 | 2023 |
Towards effective patient simulators V Liventsev, A Härmä, M Petković Frontiers in artificial intelligence 4, 798659, 2021 | 7 | 2021 |
Neurogenetic programming framework for explainable reinforcement learning V Liventsev, A Härmä, M Petković Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021 | 5 | 2021 |
Deep text prior: weakly supervised learning for assertion classification V Liventsev, I Fedulova, D Dylov International Conference on Artificial Neural Networks, 243-257, 2019 | 4 | 2019 |
BF++: a language for general-purpose neural program synthesis V Liventsev, A Härmä, M Petkovic arXiv preprint arXiv:2101.09571, 2021 | 3 | 2021 |
Tree Variational Autoencoder for Code V Liventsev, S de Bruin, A Härmä | 2* | |
PhilHumans: Benchmarking Machine Learning for Personal Health V Liventsev, V Kumar, APS Susaiyah, Z Wu, I Rodin, A Yaar, S Baloccu, ... arXiv preprint arXiv:2405.02770, 2024 | | 2024 |
Intensive Care as One Big Sequence Modeling Problem V Liventsev, T Fritz arXiv preprint arXiv:2402.17501, 2024 | | 2024 |
Comparison between program synthesis with large language models and model predictive control for buildings optimal operation L Zabala, V Liventsev, J Febres, R Sterling | | 2023 |