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EvoVGM : a deep variational generative model for evoluti..:

, In: Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics,

Remita, Amine M. ; Diallo, Abdoulaye Baniré - p. 1-10 , 2022

Link: https://dl.acm.org/doi/10.1145/3535508.3545563

RT 
T1 <cTitle>Proceedings of the 13th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics</cTitle>: T1 <Title>EvoVGM				: a deep variational generative model for evolutionary parameter estimation</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3545563&Exemplar=1&LAN=DE
				
A1 Remita, Amine M.
A1 Diallo, Abdoulaye Baniré
PB ACM
YR 2022
K1 EvoVGM
K1 deep neural networks
K1 evolutionary model
K1 latent variables
K1 substitution model
K1 variational generative model
K1 variational inference
K1 Applied computing
K1 Life and medical sciences
K1 Computational biology
K1 Molecular evolution
K1 Molecular sequence analysis
K1 Mathematics of computing
K1 Probability and statistics
K1 Probabilistic inference problems
K1 Bayesian computation
K1 Computing methodologies
K1 Machine learning
K1 Machine learning approaches
K1 Neural networks
K1 Learning latent representations
K1 Learning in probabilistic graphical models
K1 Latent variable models
K1 Probabilistic reasoning algorithms
K1 Variational methods
SP 1
OP 10
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3535508.3545563
DO https://dl.acm.org/doi/10.1145/3535508.3545563
SF ELIB - SuUB Bremen

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