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Thanks for posting the code! It is very useful.
I have some confusion about the loss function. The loss function used in this method was Kullback-Leibler divergence. It suppose to be non-negative. H…
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Hi @ageron ,
cell n°44 @ https://github.com/ageron/handson-ml2/blob/master/17_autoencoders_and_gans.ipynb,
you build a KLDivergence Layer,
but the formula you use is little difficult to understan…
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Is Statistical Connectomics related in some way to random graph theory? Would it be possible to calculate a Kullback-Leibler or other type of divergence between two graphs to compare them?
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Is the implementation correct?
The computation of KL Divergence in your code is
```
# Kullback Leibler divergence
self.e_loss = -0.5 * tf.reduce_sum(1 + self.log_sigma_sq - tf.square(sel…
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https://hsinjhao.github.io/2019/05/22/KL-DivergenceIntroduction/#more
KL散度简介KL散度的概念来源于概率论和信息论中。KL散度又被称为：相对熵、互熵、鉴别信息、Kullback熵、Kullback-Leible散度(即KL散度的简写)。在机器学习、深度学习领域中，KL散度被广泛运用于变分自编码器中(Variational…
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Hello, I see that you use Kullback-Leibler divergence function in the formula for feature regularization loss in your paper. Why you are using the cross entropy to calculate the feature regularization…
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The current tutorials cover a majority of MCMC. Could we get one for variational inference? The edward tutorial on Supervised Learning shows how to run inference using Kullback-Leibler divergence. It …
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Just a small-ish roadmap to different Optimizers and losses we can look at to add :
Optimizers:-
- [x] Adam
- [x] Adagrad
- [x] SGD
- [x] RMSprop
- [x] AdaDelta
~- [x] Riemann SGD~ Removed b…
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After running the evaluation with onet_pretrained.yaml, I noticed that the Kullback–Leibler divergence is always 0,
How did you measure it? and where can I find more info on this evaluation? Why i…
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I think it would be nice and very useful in the sequential analysis to not only have a plot of the BF and how it developes over time while the data come in, but also the Kullback-Leibler distance betw…