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Include new uncertainty quantification feature following Oak Ridge National Lab presentation
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# UQ4ML – Uncertainty Quantification Techniques in Machine Learning Models
This session focuses on uncertainty quantification (UQ) techniques in machine learning models and their applications acros…
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### Name
Vivek Srikrishnan
### Email
vs498@cornell.edu
### Institution
Cornell University
### Referral
I'm a coauthor!
### Topic Area
Uncertainty Quantification
### Description
Tutorial not…
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I went deep through the rabbit hole of uncertainty quantification in forecasting of neural networks. A faster method of quantifying uncertainty than what was proposed by [Lapeyrolerie et al, 2022](htt…
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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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**Topic**
Uncertainty quantification: How is uncertainty measured, how do you validate it, how is it used?
**How is the topic relevant to the tric-dt themes?**
This topic came up in several conve…
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Hi nice paper.
I was wondering from the methods, could you get a quantified number of uncertainty for the model output?
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Given that uncertainty quantification is used often in many settings when confidence in predictions is required it would be nice to include a task in Flaml that tackles this setting.
There are a c…
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Uncertainty quantification based on Gal et al.'s work [https://arxiv.org/pdf/1703.04977.pdf] has been implemented on many models since issues #1119 and #1211 . However, for many of the newer models, e…
z-q-y updated
4 months ago
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https://doi.org/10.1088/0957-0233/24/4/045302 , **page 6 shows the process:**
After image deformation: multiply images, then threshold. White pixels represent particles tht are present in A and B. Us…