Results 41 to 50 of about 21,280,601 (295)
Boosting Graph Structure Learning with Dummy Nodes [PDF]
International Conference on Machine Learning, 2022 With the development of graph kernels and graph representation learning, many superior methods have been proposed to handle scalability and oversmoothing issues on graph structure learning.
Xin Liu +3 more
semanticscholar +1 more source
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
doaj +1 more source
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
doaj +1 more source
Multilevel selection as Bayesian inference, major transitions in individuality as structure learning [PDF]
Royal Society Open Science, 2019 Complexity of life forms on the Earth has increased tremendously, primarily driven by subsequent evolutionary transitions in individuality, a mechanism in which units formerly being capable of independent replication combine to form higher-level ...
Dániel Czégel +2 more
doaj +1 more source
Machine learning cosmological structure formation [PDF]
, 2018 We train a machine learning algorithm to learn cosmological structure formation from N-body simulations. The algorithm infers the relationship between the initial conditions and the final dark matter haloes, without the need to introduce approximate halo
Lochner, Michelle +3 more
core +2 more sources
Structure Learning of Hamiltonians from Real-Time Evolution [PDF]
IEEE Annual Symposium on Foundations of Computer ScienceWe study the problem of Hamiltonian structure learning from real-time evolution: given the ability to apply $e^{-\mathrm{i}Ht}$ for an unknown local Hamiltonian $H=\Sigma_{a=1}^{m}\lambda_{a}E_{a}$ on $n$ qubits, the goal is to recover $H$.
Ainesh Bakshi +3 more
semanticscholar +1 more source
Multiple conformations facilitate PilT function in the type IV pilus
Nature Communications, 2019 Bacterial type IV pilus-like systems catalyse the formation of pilin fibres but it is unknown how they are powered. Here, the authors present crystal and cryo-EM structures of the hexameric motor ATPases PilB and PilT from Type IVa Pilus that reveal ...
Matthew McCallum +6 more
doaj +1 more source
Hybrid Optimization Algorithm for Bayesian Network Structure Learning
Information, 2019 Since the beginning of the 21st century, research on artificial intelligence has made great progress. Bayesian networks have gradually become one of the hotspots and important achievements in artificial intelligence research.
Xingping Sun +5 more
doaj +1 more source
Learning the Structure for Structured Sparsity [PDF]
, 2015 Structured sparsity has recently emerged in statistics, machine learning and signal processing as a promising paradigm for learning in high-dimensional settings.
Bach, Francis, Shervashidze, Nino
core +6 more sources
A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm
International Journal of Interactive Multimedia and Artificial Intelligence, 2023 Bayesian networks are analytical models that may represent probabilistic dependent connections among variables and are useful in machine learning for generating knowledge structure. Due to the vastness of the solution space, learning Bayesian network (BN)
Hoshang Qasim Awla +2 more
doaj +1 more source