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Explore how it performs against other algorithms, both in the sparse setting (using inducing points) and non-sparse and with gaussian and non-gaussian likelihoods
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#### Summary:
Stan defines machine-readable return types (error codes, exceptions, ....??) in a variety of places and they don't necessarily agree.
#### Description:
1) [stan::services::error_c…
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hello fellow developers, it appears that the tf.keras and tfp.tfp.layers. are not compatible
i have this code="
**num_inducing_points = 40
model = tf.keras.Sequential([
tf.keras.layers.InputLa…
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#### Summary
Any object returned by sampling should allow `.summary()` to be called on it to report mean, sd, MCMC SE, quantiles, and R-hat. This includes objects returned by
* `CmdStanMCMC`: M…
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Hello again. I was trying out pathfinder algorithm for SEM model and my code works for different methods (HMC, Variational inference), however it always fails when using Pathfinder algorithm.
My c…
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Here is the simplest example.
```python3
fc1 = BayesianLinear(1, 1)
print(list(fc1.parameters()))
pytorch_total_params = sum(p.numel() for p in fc1.parameters() if p.requires_grad)
```
The outpu…
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Hi,
I'm trying to build a variational auto-encoder. I want to create a layer that sampling the inputs from previous layer. Here's my implementation.
`from neon.layers.layer import Layer, interpr…
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Following models are proposed to be added:
- [x] Adversarial Autoencoders
- [x] ~Adversarial Variational Bayes~
- [ ] ALI/BiGAN
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https://arxiv.org/abs/1506.05254
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Using `Exponential(x)` distribution should be equivalent to using `Gamma(1,x)`, yet I'm getting different results when I use one or the other as the approximation of the posterior in `KLqp`. For exam…