tensorflow / tensorflow/probability
Sample different weights for examples within a batch
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Description
If I'm not mistaking currently bayesian Conv2d samples single kernel for all examples within a batch. In principle sampling different kernels for each of the examples could make MC estimates better. This requires batched convolution where the kernel has batch dimention too. Possible approach is here:
https://stackoverflow.com/questions/42068999/tensorflow-convolutions-with-different-filter-for-each-sample-in-the-mini-batch
Does it worth to implement such feature?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by locating the current Bayesian Conv2d implementation and understanding how it samples one kernel for a batch. Review the linked TensorFlow convolution approach for per-example filters. Done means agreeing on the feature scope and implementing per-example kernel sampling with coverage for the resulting MC estimates; no file or test is named here.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100