Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Speranskaya, Marina"'
We propose to use reinforcement learning to inform transformer-based contextualized link prediction models by providing paths that are most useful for predicting the correct answer. This is in contrast to previous approaches, that either used reinfor
Externí odkaz:
http://arxiv.org/abs/2211.10688
Publikováno v:
Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021)
Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a specific model architecture. In this work, we introduce Knodle, a sof
Externí odkaz:
http://arxiv.org/abs/2104.11557
Knowledge base completion (KBC) methods aim at inferring missing facts from the information present in a knowledge base (KB) by estimating the likelihood of candidate facts. In the prevailing evaluation paradigm, models do not actually decide whether
Externí odkaz:
http://arxiv.org/abs/2102.06145
Autor:
Ayginin, Andrey A.1,2, Pimkina, Ekaterina V.1, Matsvay, Alina D.1,2, Speranskaya, Anna S.1,3, Safonova, Marina V.1, Blinova, Ekaterina A.1, Artyushin, Ilya V.3, Dedkov, Vladimir G.1,4, Shipulin, German A.1, Khafizov, Kamil1,2
Publikováno v:
Advances in Virology. 8/12/2018, p1-10. 10p.
