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 |
|---|---|---|
| ChangYong-Oh/RadiusDirectionPosteriors | canonical | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| train | Ran | ChangYong-Oh/RadiusDirectionPosteriors/RadiusDirectionPosteriors/train_double_uci.py pointer only (licence: NONE) · get_code("bdab7af08bcc2f37") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose a new variational family for Bayesian neural networks. We decompose the variational posterior into two components, where the radial component captures the strength of each neuron in terms of its magnitude; while the directional component captures the statistical dependencies among the weight parameters. The dependencies learned via the directional density provide better modeling performance compared to the widely-used Gaussian mean-field-type variational family. In addition, the strength of input and output neurons learned via the radial density provides a structured way to compress neural networks. Indeed, experiments show that our variational family improves predictive performance and yields compressed networks simultaneously.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1902.02603")
get_code_for_paper("1902.02603")
have("1902.02603")
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