Results 11 to 20 of about 3,865,632 (281)
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 Lynch, Frederik Mallmann-Trenn
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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.
Dillon, Barry M +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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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
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
doaj +1 more source
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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Multiscale Causal Structure Learning
, 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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Synthesis of a novel photoactivatable glucosylceramide cross-linker
Journal of Lipid Research, 2016 The biosynthesis of glucosylceramide (GlcCer) is a key rate-limiting step in complex glycosphingolipid (GSL) biosynthesis. To further define interacting partners of GlcCer, we have made a cleavable, biotinylated, photoreactive GlcCer analog in which the ...
Monique Budani +3 more
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Applied Sciences, 2019 This paper proposes the variable chromosome genetic algorithm (VCGA) for structure learning in neural networks. Currently, the structural parameters of neural networks, i.e., number of neurons, coupling relations, number of layers, etc., have mostly been
Kang-moon Park +2 more
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Learning Bayesian Networks That Enable Full Propagation of Evidence
IEEE Access, 2020 This paper builds on recent developments in Bayesian network (BN) structure learning under the controversial assumption that the input variables are dependent.
Anthony C. Constantinou
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