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- [x] fix the logical issue: we cannot just push down the choices as we want, instead we need to have optimization rules which are the combination of choice calculus optimization rules and relational …
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The base class `variational::base_family` has no virtual methods and the one function it does define uses no member variables or functions, so can be made standalone outside of the class. Therefore, …
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General meta-programs for VI:
- Define a subset of variables that are assumed to always be there.
- Target those with the VI
- "Gen provides meta-programs for black box variational inference"
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My data dimensions are [B, N, D], the first dimension is batchsize, the second dimension is the sequence length in the sample, and the third dimension is the feature channel.
Before feeding into the…
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# 🐛 Bug
I am using the sparse variational GPyTorch framework to perform 7500 tasks. I have 4800 data points, and I am using batch sizes (so both the inout and output matrices have dimension (4800…
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As someone involved in the recent tsdate releases, I'm wondering how tsinfer+tsdate_v0.2.1 performs (e.g. compared to Relate). The recommended way to perform ARG inference now is
```python
import …
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# GP
- [GP for big data: Hensmen (2013)][2]
- [Stochastic Variational Inference for Fully Bayesian Sparse Gaussian Process Regression Models: Yu (2017)][3]
# SVI
- [Variational Auto-encoder][4]…
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There are two types of autoencoder: AutoEncoder (AE) and Variational AutoEncoder (VAE). For details, please refer to the following article:
https://towardsdatascience.com/difference-between-autoencod…
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