Results 11 to 20 of about 691,007 (262)
Adaptive learning is structure learning in time. [PDF]
Neurosci Biobehav Rev, 2021 People use information flexibly. They often combine multiple sources of relevant information over time in order to inform decisions with little or no interference from intervening irrelevant sources. They adjust the degree to which they use new information over time rationally in accordance with environmental statistics and their own uncertainty.
Yu LQ, Wilson RC, Nassar MR.
europepmc +4 more sources
Learning the Structure for Structured Sparsity [PDF]
IEEE Transactions on Signal Processing, 2015 Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings. All existing methods for learning under the assumption of structured sparsity rely on prior knowledge on how to weight (or how to penalize) individual subsets of variables during the subset ...
Shervashidze, Nino, Bach, Francis
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Learning hierarchically-structured concepts [PDF]
Neural Networks, 2021 We study the question of how concepts that have structure get represented in the brain. Specifically, we introduce a model for hierarchically structured concepts and we show how a biologically plausible neural network can recognize these concepts, and how it can learn them in the first place.
Nancy A. Lynch, Frederik Mallmann-Trenn
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Structure Learning of H-Colorings [PDF]
ACM Transactions on Algorithms, 2020 We study the following structure learning problem forH-colorings. For a fixed (and known) constraint graphHwithqcolors, given access to uniformly randomH-colorings of an unknown graphG=(V,E), how many samples are required to learn the edges of G? We give a characterization of the constraint graphs Hfor which the problem is identifiable for every Gand ...
Antonio Blanca +3 more
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The Structuration of Organizational Learning [PDF]
Human Relations, 2003 Although it is currently common to speak of organizational learning, this notion is still surrounded by conceptual confusion. It is unclear how notions like learning, knowledge and cognitive activities can be applied to organizations. Some authors have tried to unravel the conceptual and ontological problems by giving an account of the role of ...
Berends, J.J. +2 more
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A Novel BN Learning Algorithm Based on Block Learning Strategy
Sensors, 2020 Learning accurate Bayesian Network (BN) structures of high-dimensional and sparse data is difficult because of high computation complexity. To learn the accurate structure for high-dimensional and sparse data faster, this paper adopts a divide and ...
Xinyu Li, Xiaoguang Gao, Chenfeng Wang
doaj +1 more source
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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Structure-preserving deep learning [PDF]
European Journal of Applied Mathematics, 2021 Over the past few years, deep learning has risen to the foreground as a topic of massive interest, mainly as a result of successes obtained in solving large-scale image processing tasks. There are multiple challenging mathematical problems involved in applying deep learning: most deep learning methods require the solution of hard optimisation problems,
CELLEDONI, E. +6 more
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An Adaptive Unsupervised Feature Selection Algorithm Based on MDS for Tumor Gene Data Classification
Sensors, 2021 Identifying the key genes related to tumors from gene expression data with a large number of features is important for the accurate classification of tumors and to make special treatment decisions.
Bo Jin +6 more
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Atomistic structure learning [PDF]
The Journal of Chemical Physics, 2019 One endeavor of modern physical chemistry is to use bottom-up approaches to design materials and drugs with desired properties. Here, we introduce an atomistic structure learning algorithm (ASLA) that utilizes a convolutional neural network to build 2D structures and planar compounds atom by atom.
Mathias S. Jørgensen +6 more
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