Results 21 to 30 of about 81,262 (253)
SoftwareX, 2022 The application of deep learning models to increasingly complex contexts has led to a rise in the complexity of the models themselves. Due to this, there is an increase in the number of hyper-parameters (HPs) to be set and Hyper-Parameter Optimization ...
Michele Fraccaroli +2 more
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eLife, 2022 The lateral prefrontal cortex (LPFC) of humans enables flexible goal-directed behavior. However, its functional organization remains actively debated after decades of research.
Majd Abdallah +3 more
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MATRIX-VECTOR ALGORITHMS OF LOCAL POSTERIORI INFERENCE IN ALGEBRAIC BAYESIAN NETWORKS ON QUANTA PROPOSITIONS [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2015 Posteriori inference is one of the three kinds of probabilistic-logic inferences in the probabilistic graphical models theory and the base for processing of knowledge patterns with probabilistic uncertainty using Bayesian networks. The paper deals with a
A. A. Zolotin +2 more
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Argumentation-Based Query Answering under Uncertainty with Application to Cybersecurity
Big Data and Cognitive Computing, 2022 Decision support tools are key components of intelligent sociotechnical systems, and their successful implementation faces a variety of challenges, including the multiplicity of information sources, heterogeneous format, and constant changes.
Mario A. Leiva +3 more
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A Unified Approach to Semantic Information and Communication Based on Probabilistic Logic
IEEE Access, 2022 Traditionally, studies on technical communication (TC) are based on stochastic modeling and manipulation. This is not sufficient for semantic communication (SC) where semantic elements are logically connected, rather than stochastically correlated.
Jinho Choi, Seng W. Loke, Jihong Park
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Probabilistic description logic programs [PDF]
International Journal of Approximate Reasoning, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Logical Rule-Based Knowledge Graph Reasoning: A Comprehensive Survey
Mathematics, 2023 With its powerful expressive capability and intuitive presentation, the knowledge graph has emerged as one of the primary forms of knowledge representation and management.
Zefan Zeng, Qing Cheng, Yuehang Si
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A History of Probabilistic Inductive Logic Programming
Frontiers in Robotics and AI, 2014 The field of Probabilistic Logic Programming (PLP) has seen significant advances in the last 20 years, with many proposals for languages that combine probability with logic programming.
Fabrizio eRiguzzi +2 more
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Probabilistic Inductive Logic Programming [PDF]
, 2004 Probabilistic inductive logic programming, sometimes also called statistical relational learning, addresses one of the central questions of artificial intelligence: the integration of probabilistic reasoning with first order logic representations and machine learning.
De Raedt, Luc, Kersting, Kristian
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MAP Inference for Probabilistic Logic Programming [PDF]
Theory and Practice of Logic Programming, 2020 AbstractIn Probabilistic Logic Programming (PLP) the most commonly studied inference task is to compute the marginal probability of a query given a program. In this paper, we consider two other important tasks in the PLP setting: the Maximum-A-Posteriori (MAP) inference task, which determines the most likely values for a subset of the random variables ...
Bellodi E. +3 more
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