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- [ ] Look for information on automatic hyperparameter tuning optimization and its viability for our project
- [ ] Define hyperparameters to be optimized for
- [ ] Test new methods like population-b…
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Dear all,
I wanted to point out to you:
1) a paper released today comparing various methods for characterizing variability in gappy X-ray light curves in the Poisson regime, with potentially var…
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# Description
Enhance the current [Randomized Benchmarking experiments](https://github.com/Qiskit/qiskit-ignis/tree/master/qiskit/ignis/verification/randomized_benchmarking) in Qiskit-Ignis with ad…
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# Uncertainty Quantification of ML models: From Introduction to Advanced
# Responsible person(s)
Sebastian Starke, , HZDR,
Steve Schmerler, HZDR, @elcorto
Peter Steinbach, HZDR, @psteinb
G…
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Can you add the support for the Beta Mixture Model. This is a particular useful in point process estimation and one of the nice features of the `dirichletprocess` R package. This would be helpful to h…
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Work with multi-layer bayesian neural networks and compare it with more classical methods (ADVI).
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Lately, a lot of active learning methods have been developed for deep neural networks. Some of these state of the art methods are considered as a standard benchmark when comparing various active learn…
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![image](https://user-images.githubusercontent.com/30803146/70693628-7fb6a500-1cc6-11ea-86a2-2ec7f15c9bbd.png)
![image](https://user-images.githubusercontent.com/30803146/70693609-762d3d00-1cc6-1…
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@larryshamalama To summarize the pros and cons for each approach (point-treatment and binary or continuous outcome) and document published packages on their scope and flexibility. In particular, we fo…
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Suggested by @juanitorduz. Would be good to get measures of uncertainty for the non-Bayesian models. Could use:
- https://github.com/scikit-learn-contrib/MAPIE
- https://github.com/statsmodels/stats…