Flexible structure learning under uncertainty [PDF]
Frontiers in Neuroscience, 2023 Experience is known to facilitate our ability to interpret sequences of events and make predictions about the future by extracting temporal regularities in our environments.
Rui Wang +5 more
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Causal Structure Learning with Conditional and Unique Information Groups-Decomposition Inequalities [PDF]
EntropyThe causal structure of a system imposes constraints on the joint probability distribution of variables that can be generated by the system. Archetypal constraints consist of conditional independencies between variables.
Daniel Chicharro, Julia K. Nguyen
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Bayesian Network Structure Learning Method Based on Causal Direction Graph for Protein Signaling Networks [PDF]
Entropy, 2022 Constructing the structure of protein signaling networks by Bayesian network technology is a key issue in the field of bioinformatics. The primitive structure learning algorithms of the Bayesian network take no account of the causal relationships between
Xiaohan Wei, Yulai Zhang, Cheng Wang
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Causal Analysis of Physiological Sleep Data Using Granger Causality and Score-Based Structure Learning [PDF]
Sensors, 2023 Understanding how the human body works during sleep and how this varies in the population is a task with significant implications for medicine. Polysomnographic studies, or sleep studies, are a common diagnostic method that produces a significant ...
Alex Thomas +2 more
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It Is What It Isn’t: Introducing a Constraint-Based Approach to Structure Learning [PDF]
EntropyBiological cognition depends on learning-structured representations in ambiguous environments. Computational models of structure learning typically frame this as an inference problem, but often overlook the temporally extended dynamics that shape ...
Christoffer Lundbak Olesen +5 more
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Greedy structure learning from data that contain systematic missing values [PDF]
, 2022 Learning from data that contain missing values represents a common phenomenon in many domains. Relatively few Bayesian Network structure learning algorithms account for missing data, and those that do tend to rely on standard approaches that assume ...
Liu, Y +5 more
core +1 more source
Improved Local Search with Momentum for Bayesian Networks Structure Learning
Entropy, 2021 Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex ...
Xiaohan Liu +3 more
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Dynamic Bayesian Network Modeling Based on Structure Prediction for Gene Regulatory Network
IEEE Access, 2021 Gene regulatory network can intuitively reflect the interaction between genes, and an in-depth study of these relationships plays a significant role in the treatment and prevention of clinical diseases.
Luxuan Qu +6 more
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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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