Statements (22)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:algorithm
|
| gptkbp:application |
deep learning
Bayesian neural networks uncertainty estimation |
| gptkbp:category |
gptkb:Markov_Chain_Monte_Carlo
|
| gptkbp:citation |
Bayesian Learning via Stochastic Gradient Langevin Dynamics
|
| gptkbp:feature |
adds Gaussian noise to gradients
combines optimization and sampling scales to large datasets |
| gptkbp:fullName |
gptkb:Stochastic_Gradient_Langevin_Dynamics
|
| https://www.w3.org/2000/01/rdf-schema#label |
SGLD
|
| gptkbp:proposedBy |
gptkb:Yee_Whye_Teh
gptkb:Max_Welling 2011 |
| gptkbp:purpose |
sampling from posterior distributions
|
| gptkbp:referencePublicationYear |
2011
|
| gptkbp:relatedTo |
gptkb:Langevin_dynamics
Stochastic Gradient Descent |
| gptkbp:usedIn |
gptkb:machine_learning
Bayesian inference |
| gptkbp:bfsParent |
gptkb:Stochastic_Gradient_Langevin_Dynamics
|
| gptkbp:bfsLayer |
7
|