Results 11 to 20 of about 1,421,625 (273)
Frequentist parameter estimation with supervised learning [PDF]
AVS Quantum Science, 2021 Recently, there has been a great deal of interest surrounding the calibration of quantum sensors using machine learning techniques. This work explores the use of regression to infer a machine-learned point estimate of an unknown parameter. Although the analysis is necessarily frequentist—relying on repeated estimates to build up statistics—the authors ...
Samuel P. Nolan +2 more
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Dynamic Parameter Allocation in Parameter Servers [PDF]
, 2020 To keep up with increasing dataset sizes and model complexity, distributed training has become a necessity for large machine learning tasks. Parameter servers ease the implementation of distributed parameter management---a key concern in distributed ...
Gemulla, Rainer +3 more
core +1 more source
Structure Learning via Parameter Learning [PDF]
Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014 A key challenge in information and knowledge management is to automatically discover the underlying structures and patterns from large collections of extracted information. This paper presents a novel structure-learning method for a new, scalable probabilistic logic called ProPPR.
William Yang Wang +2 more
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Learning system parameters from turing patterns
Machine Learning, 2023 AbstractThe Turing mechanism describes the emergence of spatial patterns due to spontaneous symmetry breaking in reaction–diffusion processes and underlies many developmental processes. Identifying Turing mechanisms in biological systems defines a challenging problem.
David Schnörr, Christoph Schnörr
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A Study of Using Bethe/Kikuchi Approximation for Learning Directed Graphic Models
IEEE Access, 2021 This paper applies the variational methods to learn the parameters and the probability of evidence of directed graphic models (also known as Bayesian networks (BNs)) when data contains missing values.
Peng Lin, Martin Neil, Norman Fenton
doaj +1 more source
IEEE Access, 2020 Recent advances have illustrated substantial benefits from learning Bayesian networks (BNs). However, when the available data size is small, the BN parameter learning becomes a key challenge in many intelligent applications.
Yongyan Hou +4 more
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BN parameter learning based on improved QMAP algorithm under small data set conditions
Xi'an Gongcheng Daxue xuebao, 2023 Under the condition of Bayesian network (BN) small data set, the qualitative maximum a posteriori (QMAP) estimation tends to violate expert constraints, which causes the QMAP estimation to deviate the true value.
CHEN Haiyang +3 more
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Hard and Soft EM in Bayesian Network Learning from Incomplete Data
Algorithms, 2020 Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations.
Andrea Ruggieri +3 more
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Knowledge graph construction with structure and parameter learning for indoor scene design
Computational Visual Media, 2018 We consider the problem of learning a representation of both spatial relations and dependencies between objects for indoor scene design. We propose a novel knowledge graph framework based on the entity-relation model for representation of facts in indoor
Yuan Liang +4 more
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Process Monitoring Based on Multivariate Causality Analysis and Probability Inference
IEEE Access, 2018 System security is one of the key challenges of the cyber-physical systems. Bayesian approach can estimate and predict the potentially harmful factors of the general system, but it has many limitations that can lead to undesirable effects in the complex ...
Xiaolu Chen, Jing Wang, Jinglin Zhou
doaj +1 more source