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[NOTE]: # ( ^^ Provide a general summary of the request in the title above. ^^ )
## Summary
Can you please provide an example of logging inference predictions for a deep learning model (e.g. ima…
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1. Inference on Predictive and Causal Effects in High Dimensional Linear Regression Models
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> [!NOTE]
> If you have a request to support a specific method, or would like to see priority of one of the listed methods, please open a separate issue, so it won't get buried in this thread. Base…
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Consider that we are only interested in a few parameters in a (linear or non-gaussian) regression model, but have a large number of possible extra explanatory variables (confounders/controls).
What…
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related to
#2765 #4143 traditional/old statistics for covariance and correlation structures and tests
#3197 penalized and shrinkage estimator for covariances or inverse covariances
These are ju…
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# High-dimensional, unbinned deconvolution with OmniFold
One sentence description: Removing detector distortions using likelihood free inference, based on 1911.09107.
Contacts: Ben Nachman
Part…
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Most of methods in the list will be implemented in the order.
- inference for Sparse Gaussian process regression (based on JMLR 2005 "A unifying view of sparse approximate Gaussian process regression…
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(where is my code, I don't see it in #2374, not committed anywhere?)
main references are Jianqing Fan and coauthors
the following two are very recent, and the Wang/Leng article seems to have a good …
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In the paper, the dim of VAE latent is 8 or 16 and the experiments covers MLPs of 6-12 blocks. I experimented with an 8-block MLP learning 64 and 1024-dim audio data, while the model struggled to lear…
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Hi @dm13450 I am trying to get dirichletprocess working for clustering a high-dimensional data set.
For example https://web.stanford.edu/~hastie/ElemStatLearn/datasets/zip.train.gz has 256 features. …