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### Contact Details
geethen.singh@gmail.com
### Dataset description
Using a modern uncertainty quantification method, conformal prediction, we have quantified the uncertainty for the first non-null…
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Add your ideas for KPP 4.0.0 here. Everything we don't have the time to work on now but may be a nice addition in the future...
- [ ] Replace all BLAS/LAPACK functions (WAXPY, WSCAL, etc.) in integ…
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Problem: CatBoost is a great library, but it currently lacks reliable modern uncertainty quantification that is rather easy to implement using conformal prediction. https://github.com/valeman/awesome-…
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Thank you very much for providing such a useful tool! I am trying to integrate it in the testing setup for our research codes at [OpenSourceEconomics](https://github.com/OpenSourceEconomics). However,…
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@article{KIM20152904,
title = {Uncertainty quantification of ion chemistry in lean and stoichiometric homogenous mixtures of methane, oxygen, and argon},
journal = {Combustion and Flame},
volume = …
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Currently, Autogluon Tabular predicts values without giving an estimate of the uncertainty.
Are you going to integrate methods like [conformal prediction](https://arxiv.org/pdf/2005.07972.pdf) or ano…
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When TruncNormal is selected as the distribution and fitting PCE, we perform chaospy.E(approx, distribution) or chaospy.Std(approx, distribution). Finally the errors are as follow:
Traceback (most…
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Hi, I have read the part about the uncertainty quantification of neural networks, and I'm confused about the log-likelihood function you mentioned, i.e. -sum((y[:,1] - obs[:,1]).^2)/2σ^2 - sum(x.^2)/2…
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Hi!
First of all, thanks for the excellent package, and in particular also for still actively maintaining it! :-)
I have some questions regarding the bootstrapping-based uncertainty quantificati…
e-pet updated
5 months ago
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Given #648, I thought it was a good idea to open an issue for the forwarding of ensemble predictions to MD engines, which we will need relatively soon.
The design could be as simple as having a new…