Bayesian neural networks

GPTKB entity

Statements (58)
Predicate Object
gptkbp:instance_of gptkb:microprocessor
gptkb:television_channel
gptkbp:bfsLayer 3
gptkbp:bfsParent gptkb:Pyro
gptkbp:analyzes probability distributions
gptkbp:applies_to gptkb:robot
financial forecasting
natural language processing
medical diagnosis
gptkbp:based_on Bayesian inference
gptkbp:benefits deep learning models
frequentist methods
gptkbp:challenges computational complexity
model interpretability
hyperparameter optimization
gptkbp:controls small datasets
noisy data
gptkbp:developed_by gptkb:Bayesians
gptkbp:enables robust predictions
gptkbp:enhances decision making
gptkbp:facilitates model selection
https://www.w3.org/2000/01/rdf-schema#label Bayesian neural networks
gptkbp:improves generalization performance
gptkbp:integrates_with prior knowledge
gptkbp:is_compared_to traditional neural networks
gptkbp:is_evaluated_by cross-validation
posterior predictive checks
Bayesian model selection
gptkbp:is_implemented_in gptkb:Graphics_Processing_Unit
gptkb:Pyro
gptkb:Edward
gptkb:Py_Torch
gptkb:Tensor_Flow_Probability
gptkbp:is_popular_in gptkb:academic_research
machine learning community
gptkbp:is_related_to gptkb:software_framework
deep learning
ensemble methods
probabilistic programming
Gaussian processes
gptkbp:is_used_by data scientists
machine learning engineers
gptkbp:is_used_for classification tasks
regression tasks
uncertainty estimation
gptkbp:is_used_in time series analysis
active learning
transfer learning
anomaly detection
semi-supervised learning
gptkbp:provides model uncertainty
gptkbp:requires hyperparameter tuning
more computational resources
expertise in Bayesian statistics
gptkbp:training variational inference
Markov Chain Monte Carlo (MCMC)
gptkbp:utilizes prior distributions
posterior distributions