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pro vyhledávání: '"Reformat, Marek"'
In this paper, we investigate the use of probabilistic graphical models, specifically stochastic blockmodels, for the purpose of hierarchical entity clustering on knowledge graphs. These models, seldom used in the Semantic Web community, decompose a
Externí odkaz:
http://arxiv.org/abs/2408.15649
Autor:
Rezaei, Navid, Reformat, Marek Z.
Larger language models, such as GPT-3, have shown to be excellent in many tasks. However, we demonstrate that out-of-ordinary questions can throw the model off guard. This work focuses on finding answers to negated complementary questions in commonse
Externí odkaz:
http://arxiv.org/abs/2307.06794
Autor:
Costello, Jeremy, Reformat, Marek Z.
We apply reinforcement learning techniques to topic modeling by replacing the variational autoencoder in ProdLDA with a continuous action space reinforcement learning policy. We train the system with a policy gradient algorithm REINFORCE. Additionall
Externí odkaz:
http://arxiv.org/abs/2305.04843
Autor:
Rezaei, Navid, Reformat, Marek Z.
Pre-trained language models have shown excellent results in few-shot learning scenarios using in-context learning. Although it is impressive, the size of language models can be prohibitive to make them usable in on-device applications, such as sensor
Externí odkaz:
http://arxiv.org/abs/2204.11922
Publikováno v:
In Medical Engineering and Physics August 2024 130
Computational Intelligence (CI), which includes fuzzy logic (FL), neural network (NN), and evolutionary computation (EC), is an imperative branch of artificial intelligence (AI). As a core technology of AI, it plays a vital role in developing intelli
Externí odkaz:
http://arxiv.org/abs/2112.01228
Publikováno v:
In Ultrasound in Medicine & Biology November 2024 50(11):1690-1696
Autor:
Pietrasik, Marcin, Reformat, Marek
Knowledge graphs have emerged as a widely adopted medium for storing relational data, making methods for automatically reasoning with them highly desirable. In this paper, we present a novel approach for inducing a hierarchy of subject clusters, buil
Externí odkaz:
http://arxiv.org/abs/2109.13178
The Mixed-Membership Stochastic Blockmodel~(MMSB) is proposed as one of the state-of-the-art Bayesian relational methods suitable for learning the complex hidden structure underlying the network data. However, the current formulation of MMSB suffers
Externí odkaz:
http://arxiv.org/abs/2002.00901
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