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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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Structural similarity and distance in learning [PDF]
2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2011 Based on work presented at Allerton ...
Joseph Wang 0001 +2 more
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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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Machine-learning mathematical structures
International Journal of Data Science in the Mathematical Sciences, 2022 We review, for a general audience, a variety of recent experiments on extracting structure from machine-learning mathematical data that have been compiled over the years. Focusing on supervised machine-learning on labeled data from different fields ranging from geometry to representation theory, from combinatorics to number theory, we present a ...
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Japan has one of the highest dialysis prevalence rates worldwide and a shrinking, aging population. Whether dialysis burden has entered a sustained post‐peak phase or whether recent declines partly reflect pandemic‐related disruptions remains uncertain.
Hatice Şahin +2 more
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Gut microbiome and aging—A dynamic interplay of microbes, metabolites, and the immune system
FEBS Letters, EarlyView.Age‐dependent shifts in microbial communities engender shifts in microbial metabolite profiles. These in turn drive shifts in barrier surface permeability of the gut and brain and induce immune activation. When paired with preexisting age‐related chronic inflammation this increases the risk of neuroinflammation and neurodegenerative diseases.
Aaron Mehl, Eran Blacher
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Structure Learning for Directed Trees
J. Mach. Learn. Res., 2021 ISSN:1532 ...
Jakobsen, Martin Emil +3 more
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Hidden Variable Discovery Based on Regression and Entropy
MathematicsInferring causality from observed data is crucial in many scientific fields, but this process is often hindered by incomplete data. The incomplete data can lead to mistakes in understanding how variables affect each other, especially when some ...
Xingyu Liao, Xiaoping Liu
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