|
|
|
Publications in Math-Net.Ru |
Citations |
|
2025 |
| 1. |
A. S. Krylov, I. I. Vagizov, D. S. Korzh, M. Douiba, A. Guezzaz, V. N. Kokh, S. D. Erokhin, E. V. Tutubalina, O. Y. Rogov, “HAMSA: hijacking aligned compact models via stealthy automation”, Dokl. RAN. Math. Inf. Proc. Upr., 527 (2025), 449–458   |
|
2023 |
| 2. |
D. D. Bakshandaeva, D. V. Dimitrov, V. S. Arkhipkin, A. V. Shonenkov, M. S. Potanin, D. K. Karachev, A. V. Kuznetsov, A. D. Voronov, A. A. Petiushko, V. F. Davydova, E. V. Tutubalina, “Many heads but one brain: FusionBrain – a single multimodal multitask architecture and a competition”, Computer Optics, 47:1 (2023), 185–195 |
| 3. |
A. Alekseev, A. Savchenko, E. Tutubalina, E. Myasnikov, S. Nikolenko, “Blending of predictions boosts understanding for multimodal advertisements”, Zap. Nauchn. Sem. POMI, 529 (2023), 176–196 ; J. Math. Sci. (N. Y.), 285:1 (2024), 126–141 |
1
|
|
2021 |
| 4. |
A. S. Sakhovskiy, E. V. Tutubalina, “Ñross-lingual transfer learning in drug-related information extraction from user-generated texts”, Proceedings of ISP RAS, 33:6 (2021), 217–228 |
| 5. |
E. Tutubalina, S. I. Nikolenko, “Topic models with sentiment priors based on distributed representations”, Zap. Nauchn. Sem. POMI, 499 (2021), 284–301 |
| 6. |
D. Mazitov, I. Alimova, E. Tutubalina, “Named entity recognition in Russian using multi-task LSTM-CRF”, Zap. Nauchn. Sem. POMI, 499 (2021), 222–235 |
|
2018 |
| 7. |
I. S. Alimova, E. V. Tutubalina, “Entity-level classification of adverse drug reactions: a comparison of neural network models”, Proceedings of ISP RAS, 30:5 (2018), 177–196 |
2
|
|
2015 |
| 8. |
E. V. Tutubalina, “Sentiment-based topic model for mining usability issues and failures with user products”, Proceedings of ISP RAS, 27:4 (2015), 111–128 |
|
| Organisations |
|
|
