Yarin Gal
We lifted 4 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| arneschmidt/bayesian_deep_learning | reimplementation | 3 of 3 |
| kenya-sk/mc_dropout_tensorflow | reimplementation | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| f | Ran | arneschmidt/bayesian_deep_learning/regression_with_GP.py pointer only (licence: NONE) · get_code("9dc92202a028ac23") |
| k | Ran | arneschmidt/bayesian_deep_learning/regression_with_BNN.py pointer only (licence: NONE) · get_code("1dd2001e70f05aff") |
| k_se | Ran | arneschmidt/bayesian_deep_learning/regression_with_GP.py pointer only (licence: NONE) · get_code("d853188e0b72364e") |
| mc_dropout_prediction | Ran | kenya-sk/mc_dropout_tensorflow/src/mc_dropout_prediction.py pointer only (licence: NONE) · get_code("3416eab71cd2de41") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNsextracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout's uncertainty. Various network architectures and nonlinearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predictive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout's uncertainty in deep reinforcement learning.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1506.02142")
get_code_for_paper("1506.02142")
have("1506.02142")
Connect an agent — have() is free.