We lifted 1 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| thudzj/ScalableBDL | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| Bayes_ensemble | Ran | thudzj/ScalableBDL/scalablebdl/bnn_utils.py code served (permissive licence) · get_code("1544d5980d6e6050") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Despite their theoretical appealingness, Bayesian neural networks (BNNs) are left behind in real-world adoption, mainly due to persistent concerns on their scalability, accessibility, and reliability. In this work, we develop the BayesAdapter framework to relieve these concerns. In particular, we propose to adapt pre-trained deterministic NNs to be variational BNNs via cost-effective Bayesian fine-tuning. Technically, we develop a modularized implementation for the learning of variational BNNs, and refurbish the generally applicable exemplar reparameterization trick through exemplar parallelization to efficiently reduce the gradient variance in stochastic variational inference. Based on the lightweight Bayesian learning paradigm, we conduct extensive experiments on a variety of benchmarks, and show that our method can consistently induce posteriors with higher quality than competitive baselines, yet significantly reducing training overheads. Code is available at https://github.com/thudzj/ScalableBDL.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.01979")
get_code_for_paper("2010.01979")
have("2010.01979")
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