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I'm recently starting to re-implement PILCO in pytorch for better intergration with my other works. To leverage the fast prediction (KISS-GP) in gpytorch, I decided to use MCMC sampling approach to im…
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High-level API for model training similar to PyTorch Lightning.
One advantage over Python is that we don't need a separate module type to add methods to a model class (e.g. train_step() and similar…
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Hello Mihai,
I have tried your project but I get a very bad reconstructed umbrella (bundled sample), very far from DynamicFusion video of the paper. Is this project still a work in progress and not…
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The WORHP library provides additional utilities for doing sensitivity analysis once the optimal and feasible solution is found. This provides a powerful and fast way to estimate the local effects of c…
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What other models do we want to support? ANI is done and SchNet is nearing completion. What are the next top priorities?
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Hi @eleurent :-)
I'm currently working with your awesome framework. And I'm wondering whether there exits a feasible approach to define a discrete action dictonary like below?
```python
ACTIONS_A…
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With all the new Multi-Objective Optimization Algorithms coming out, I was wondering if it would be possible to implement one in OpenMC.
This would almost be synonymous with the `search_for_keff` …
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### Describe the enhancement requested
Optimisation to https://github.com/apache/arrow/issues/37511
Child of https://github.com/apache/arrow/issues/18014
When reading from Azure blob storage the …
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Is there any interest in adding a quasi-Newton based optimizer? I was thinking of porting over:
https://github.com/tensorflow/probability/blob/master/tensorflow_probability/python/optimizer/bfgs.py…
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It was pointed out in the IRIS-HEP analysis systems meeting today that it would be good to compile a list of operations in HEP workflows that are not normally differentiable, e.g. cutting and histogra…