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Simpler than annealed importance sampling, might be good enough.
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Importance sampling where a hyper sphere with radius $\beta$ is excluded. Here $\beta$ is the reliabiliy index, i.e., the distance to the design point in standard normal space.
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Hi @mattpitkin,
I was wondering if you could try this package as well:
https://github.com/JohannesBuchner/snowline https://johannesbuchner.github.io/snowline/
I haven't tested it extensively, …
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Hi Team,
Thanks for sharing the wonderful work and amazing codebase! I wonder if it is possible to remove [lighting importance sampling](https://github.com/NVlabs/nvdiffrecmc/blob/main/render/optix…
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Can you elaborate on this part of the code?
https://github.com/geek-ai/irgan/blob/master/ltr-gan/ltr-gan-pointwise/ltr_gan_d_nn_g_nn.py#L125-L129
I am trying to understand where the importance sam…
cdgiv updated
4 years ago
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This issue is about porting functionality of `chaospp` : https://github.com/jorgecarleitao/chaospp (which is forked for reference)
as well as other recent papers, see e.g. the one by @dapias and …
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Importance sampling is already implemented for the LatLongMapEnvironmentEDF.
The code needs to be refactored so that it can be reused in the OSLEnvironmentEDF.
est77 updated
4 years ago
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## Motivation
I believe the current DQN Losses don't apply importance sampling weights. This is almost always applied when using a PER Buffer.
## Solution
The PER buffer outputs "_weight" in in…
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At the moment we are doing experiments on mock datasets of 200 supernovae. What would it take to repeat these on 100,000 supernovae? Some benchmarking tests could be interesting - and let us know if w…