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
doaj +7 more sources
Causal Structure Learning: A Combinatorial Perspective. [PDF]
Found Comut Math, 2022 In this review, we discuss approaches for learning causal structure from data, also called causal discovery. In particular, we focus on approaches for learning directed acyclic graphs and various generalizations which allow for some variables to be ...
Squires C, Uhler C.
europepmc +3 more sources
Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG [PDF]
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
doaj +2 more sources
Improved Local Search with Momentum for Bayesian Networks Structure Learning [PDF]
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
doaj +2 more sources
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
doaj +2 more sources
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
doaj +2 more sources
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
doaj +2 more sources
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification [PDF]
Neural Information Processing Systems, 2023 Graph neural networks have been extensively studied for learning with inter-connected data. Despite this, recent evidence has revealed GNNs' deficiencies related to over-squashing, heterophily, handling long-range dependencies, edge incompleteness and ...
Qitian Wu +4 more
semanticscholar +1 more source
Towards Unsupervised Deep Graph Structure Learning [PDF]
The Web Conference, 2022 In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be deteriorated when noisy connections occur in the original graph structures; besides, the ...
Yixin Liu +5 more
semanticscholar +1 more source
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization [PDF]
The Web Conference, 2023 Graph Neural Networks (GNNs) are de facto solutions to structural data learning. However, it is susceptible to low-quality and unreliable structure, which has been a norm rather than an exception in real-world graphs.
Dongcheng Zou +7 more
semanticscholar +1 more source