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 |