fsp-l aplace
FSP-L APLACE: Function-Space Priors for the Laplace Approximation in Bayesian Deep Learning Tristan Cinquin
Laplace approximations are popular techniques for endowing deep networks with epistemic uncertainty estimates as they can be applied without altering the predictions of the trained network, and they scale to large models and datasets.
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Country:
- Europe > Germany > Baden-Württemberg > Tübingen Region > Tübingen (0.14)
- North America > United States > Hawaii (0.04)
- North America > Mexico (0.04)
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Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning > Uncertainty > Bayesian Inference (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.92)
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