Results 41 to 50 of about 13,423,347 (294)
Journal of Statistical Software, 2017 It is well known in the literature that the problem of learning the structure of Bayesian networks is very hard to tackle: Its computational complexity is super-exponential in the number of nodes in the worst case and polynomial in most real-world ...
Marco Scutari
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Multiscale Causal Structure Learning
Trans. Mach. Learn. Res., 2022 The inference of causal structures from observed data plays a key role in unveiling the underlying dynamics of the system. This paper exposes a novel method, named Multiscale-Causal Structure Learning (MS-CASTLE), to estimate the structure of linear causal relationships occurring at different time scales. Differently from existing approaches, MS-CASTLE
Gabriele D'Acunto +2 more
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IEEE Access, 2021 At present, in the application of Bayesian network (BN) structure learning algorithm for structure learning, the network scale increases with the increase of number of nodes, resulting in a large scale of structure search space, which is difficult to ...
Kun Liu, Yani Cui, Jia Ren, Peiran Li
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Tractable Uncertainty for Structure Learning
CoRR, 2022 ICML 2022 (long talk); 20 ...
Benjie Wang 0001 +2 more
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Temporal context and latent state inference in the hippocampal splitter signal
eLife, 2023 The hippocampus is thought to enable the encoding and retrieval of ongoing experience, the organization of that experience into structured representations like contexts, maps, and schemas, and the use of these structures to plan for the future. A central
Éléonore Duvelle +2 more
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Scholarpedia, 2013 Structural learning in motor control refers to a metalearning process whereby an agent extracts (abstract) invariants from its sensorimotor stream when experiencing a range of environments that share similar structure. Such invariants can then be exploited for faster generalization and learning-to-learn when experiencing novel, but related task ...
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Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG
Journal of Statistical Software, 2023 The R package BiDAG implements Markov chain Monte Carlo (MCMC) methods for structure learning and sampling of Bayesian networks. The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior ...
Polina Suter +3 more
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Multilevel selection as Bayesian inference, major transitions in individuality as structure learning [PDF]
Royal Society Open Science, 2019 Complexity of life forms on the Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level ...
Dániel Czégel +2 more
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Structure learning and the Occam's razor principle: A new view of human function acquisition
Frontiers in Computational Neuroscience, 2014 We often encounter pairs of variables in the world whose mutual relationship can be described by a function. After training, human responses closely correspond to these functional relationships.
Devika eNarain +6 more
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, 2020 Very recently, the unexpected combination of data structures and machine learning has led to the development of a new area of research, called learned data structures. Their distinguishing trait is the ability to reveal and exploit patterns and trends in the input data for achieving more efficiency in time and space, compared to previously known data ...
Paolo Ferragina, Giorgio Vinciguerra
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