Results 101 to 110 of about 118,631 (311)
Propagation Computation for Mixed Bayesian Networks Using Minimal Strong Triangulation
MathematicsIn recent years, mixed Bayesian networks have received increasing attention across various fields for probabilistic reasoning. Though many studies have been devoted to propagation computation on strong junction trees for mixed Bayesian networks, few have
Yao Liu +3 more
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Modular network construction using eQTL data: an analysis of computational costs and benefits
Frontiers in Genetics, 2014 Background: In this paper, we consider analytic methods for the integrated analysis of genomic DNA variation and mRNA expression (also named as eQTL data), to discover genetic networks that are associated with a complex trait of interest.
Yen-Yi eHo +2 more
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Bayesian Approach to Network Modularity
Physical Review Letters, 2008 We present an efficient, principled, and interpretable technique for inferring module assignments and for identifying the optimal number of modules in a given network. We show how several existing methods for finding modules can be described as variant, special, or limiting cases of our work, and how the method overcomes the resolution limit problem ...
Hofman, Jake M., Wiggins, Chris H.
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ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals
Advanced Science, EarlyView.The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray +3 more
wiley +1 more source
Transformers are Bayesian Networks
CoRRTransformers are the dominant architecture in AI, yet why they work remains poorly understood. This paper offers a precise answer: a transformer is a Bayesian network. We establish this in five ways. First, we prove that every sigmoid transformer with any weights implements weighted loopy belief propagation on its implicit factor graph.
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Improved learning of Bayesian networks
CoRR, 2001 The search space of Bayesian Network structures is usually defined as Acyclic Directed Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of Bayesian Networks is ordered by DAG Markov model inclusion and it is natural to consider that a good search policy should take this into account.
Kocka, T., Castelo Valdueza, R.
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Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers
Advanced Science, EarlyView.FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao +9 more
wiley +1 more source
American universities in the Middle East: A student’s perspective
Cogent Education, 2018 Many American universities located in the Middle East try to offer the stamp of higher quality in education that the United States provides and delivers. These institutions are doing an incredible job of providing opportunity for youths of that region to
Linda Smail, Ginger Silvera
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Reliable and Efficient Inference of Bayesian Networks from Sparse Data by Statistical Learning Theory [PDF]
, 2003 Dominik Janzing, Daniel Herrmann
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Bayesian networks for fMRI: A primer
NeuroImage, 2014 Bayesian network analysis is an attractive approach for studying the functional integration of brain networks, as it includes both the locations of connections between regions of the brain (functional connectivity) and more importantly the direction of the causal relationship between the regions (directed functional connectivity).
Jeanette A. Mumford, Joseph D. Ramsey
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