Results 31 to 40 of about 13,423,347 (294)
Structured Priors for Structure Learning
CoRR, 2012 Traditional approaches to Bayes net structure learning typically assume little regularity in graph structure other than sparseness. However, in many cases, we expect more systematicity: variables in real-world systems often group into classes that predict the kinds of probabilistic dependencies they participate in.
Vikash Mansinghka 0001 +3 more
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Multiple conformations facilitate PilT function in the type IV pilus
Nature Communications, 2019 Bacterial type IV pilus-like systems catalyse the formation of pilin fibres but it is unknown how they are powered. Here, the authors present crystal and cryo-EM structures of the hexameric motor ATPases PilB and PilT from Type IVa Pilus that reveal ...
Matthew McCallum +6 more
doaj +1 more source
Hybrid Optimization Algorithm for Bayesian Network Structure Learning
Information, 2019 Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun +5 more
doaj +1 more source
Multiscale causal structure learning [PDF]
, 2023 Causal structure learning methods are vital for unveiling causal relationships embedded into observed data. However, the state of the art suffers a major limitation: it assumes that causal interactions occur only at the frequency at which data is ...
Sergio Barbarossa +2 more
core
Bayesian Learning of Markov Network Structure [PDF]
, 2006 We propose a simple and efficient approach to building undirected probabilistic classification models (Markov networks) that extend naive Bayes classifiers and outperform existing directed probabilistic classifiers (Bayesian networks) of similar ...
Rish, Irina +3 more
core +2 more sources
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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Structured Apprenticeship Learning [PDF]
, 2012 We propose a graph-based algorithm for apprenticeship learning when the reward features are noisy. Previous apprenticeship learning techniques learn a reward function by using only local state features. This can be a limitation in practice, as often some features are misspecified or subject to measurement noise.
Abdeslam Boularias +2 more
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Learning the Structure of Event Sequences [PDF]
Journal of Experimental Psychology: General, 1990 How is complex sequential material acquired, processed, and represented when there is no intention to learn? Two experiments exploring a choice reaction time task are reported. Unknown to Ss, successive stimuli followed a sequence derived from a "noisy" finite-state grammar.
Cleeremans, Axel, McClelland, James L.
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Inducing structure in reward learning by learning features
The International Journal of Robotics Research, 2022 Reward learning enables robots to learn adaptable behaviors from human input. Traditional methods model the reward as a linear function of hand-crafted features, but that requires specifying all the relevant features a priori, which is impossible for real-world tasks.
Andreea Bobu +3 more
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Learning Latent Jet Structure [PDF]
Symmetry, 2021 We summarize our recent work on how to infer on jet formation processes directly from substructure data using generative statistical models. We recount in detail how to cast jet substructure observables’ measurements in terms of Bayesian mixed membership models, in particular Latent Dirichlet Allocation.
Barry M. Dillon +3 more
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