Shypd.aiThe largest open index of AI toolsDropoutUncertaintyExps provides code for uncertainty experiments used in the paper "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning." It includes adaptations based on community feedback and uses UCI machine learning repository datasets.
Trending
Also listed in
About
DropoutUncertaintyExps is an open-source project containing the experimental code for the paper "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning." The repository provides a framework for researchers to replicate and build upon the uncertainty experiments, with adaptations reflecting community feedback and bug fixes. It is based on José Miguel Hernández-Lobato's work on probabilistic backpropagation for scalable learning of Bayesian Neural Networks. The code utilizes datasets from the UCI machine learning repository, with specific data splits to ensure comparability of results. It details the methodology for hyperparameter tuning using grid-search and reports RMSE and log-likelihood metrics for various datasets, offering a valuable resource for academic research in deep learning uncertainty.
Capabilities
Pricing & Plans
Open Source
Free
FAQs
Trending
