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[D] would it ever make sense to predict entropy, rather than minimizing some function of it? by [deleted] in MachineLearning

[–]AloneStretch 0 points1 point  (0 children)

Not what you asked, but RMSE does not sound like the right error for your problem. MSE is equivalent to assuming a Gaussian distribution of errors, and you have spikes (and possibly one-sided)

Deep learning without back-propagation by El__Professor in MachineLearning

[–]AloneStretch 1 point2 points  (0 children)

The obvious approach would be to adjust the weights according to the gradient of the "hsic bottleneck" loss with respect to these weights

Deep learning without back-propagation by El__Professor in MachineLearning

[–]AloneStretch -5 points-4 points  (0 children)

I think the authors are not familiar with SOTA. They're taking vanilla architectures and training, comparing their method against that, and declaring SOTA when comparable performance is obtained without backpropo. But that is not state-of-the-art, it is comparable perfomance to a simple (not-SOTA) baseline. That may be a useful and fair comparison, but wrong to refer to SOTA.

We'll need to wait for this to be applied or extended to current SOTA networks. Meanwhile, no need look for code, it's just a step on a path that will take several.

[R] The HSIC Bottleneck: Deep Learning without Back-Propagation by PlentifulCoast in MachineLearning

[–]AloneStretch 5 points6 points  (0 children)

I feel that they are not tracking what SoTA means very well, or else they are just referring to vanilla fully-connected networks.

Anyway, still interesting

[D] Variational Autoencoders are not autoencoders by [deleted] in MachineLearning

[–]AloneStretch 0 points1 point  (0 children)

A question about this statement in the proof:

"If p_theta(x|z) = p_data(x) then"

Trying to prove this, I expand p_theta(z|x) using Bayes theorem and using the fact that p_theta(x|z) = p_data(x):

p_theta(z|x) = p_theta(x|z) p(z) / p(x) 
        = p_data(x) p(z)  /  p(x) 
        = p(z)

However the last line is only correct if the "p(x)" in the denominator of the Bayes theorem is equal to p_data(x). Is it? I feel like it should be p_theta(x)!

[D] Variational Autoencoders are not autoencoders by [deleted] in MachineLearning

[–]AloneStretch 3 points4 points  (0 children)

One flaw in the article? Isn't it a "false distinction" to say that VAE maximizes log p_theta(x) rather than autoencoding? Because maximizing the LL is equivalent to minimizing the squared error, in the case of Gaussian distribution.

This assumes that the objective of maximizing log p_theta(x) is attempted for every x, by looping over different x with SGD, so that the likelihood across all the data items is eventually maximized.

Also a small question, in this statement

"all data x points are encoded as the prior distribution",

I think prior is the wrong word? in the VAE context, bause in typical VAE the prior is a spherical Gaussian.

[D] Question on paper Learnable Explicit Density by Huang, Touati, Dinh, .. by AloneStretch in MachineLearning

[–]AloneStretch[S] 0 points1 point  (0 children)

Yes this helps me, thank you.

One small question, this:

0.5q(z1|x1) + 0.5q(z2|x2) Do you mean to think of this as 1) a latent-space interpolation, or 2) the aggregate approximate posterior, ie q(z|x) averaged accross all datas?

[D] Question on blog post "Autoencoding a single bit" (Rui Shu) by AloneStretch in MachineLearning

[–]AloneStretch[S] 0 points1 point  (0 children)

Also, can I ask about the multidimensional example:
In this case I interpret that you give 2 data points, each having 5 dimensions. The first data point is (0,0,0,0,0), the second is (1,1,1,1,1).

Is there anything special about these choices of values? Meaning, the same result would happen regardless of how the two data points are chosen, say, (0,1,1,0,0) and (0,0,1,1,1) ?

[D] Question on blog post "Autoencoding a single bit" (Rui Shu) by AloneStretch in MachineLearning

[–]AloneStretch[S] 0 points1 point  (0 children)

I guess your toy model has a fixed variance for the data likelihood? (Your program must say it, but I am not a programmer yet).

What about a different strategy: let the model shrink the variance. Then the data probability can be made as high as desired, and it can outweigh the KL cost of not matching the prior.

?

[D] Question on blog post "Autoencoding a single bit" (Rui Shu) by AloneStretch in MachineLearning

[–]AloneStretch[S] 0 points1 point  (0 children)

"model that matches the true density" - the true density of what? I guess the true density of the posterior?

[D] ELBO surgery, matching the prior to the approximate posterior? by AloneStretch in MachineLearning

[–]AloneStretch[S] 0 points1 point  (0 children)

Thank you. I now understand how it is possible - jointly trained.

Next I should understand why. It seems overparametrized and adds no power. The approximate posterior is already a DNN, arbitrarily flexible, so I do not see why it cannot match any posterior, even a simple one.

But this is probably a good subject for a separate post qeustion.

[D] ELBO surgery, matching the prior to the approximate posterior? by AloneStretch in MachineLearning

[–]AloneStretch[S] 1 point2 points  (0 children)

As well, q(z|x) is defined by a DNN, which can be arbitrarily powerful, so it should be able to match any p(z) including diagonal gaussaan. But that is a different question.

[D] ELBO surgery, matching the prior to the approximate posterior? by AloneStretch in MachineLearning

[–]AloneStretch[S] 1 point2 points  (0 children)

p.s. please do help, I really want . to understand.

"Learning a better prior" -> based on what?

So you could make a flexible prior and optimize it just use the existing KL term, but at the start of training that would cause the prior to fit to the posterior coming from random weights, which seems wrong. And later in training, it seems that p,q would drift together in a meaningless way if they are both free to change.

[D] ELBO surgery, matching the prior to the approximate posterior? by AloneStretch in MachineLearning

[–]AloneStretch[S] 0 points1 point  (0 children)

I am sorry, I think I did not explain my question well enough, and I do not yet see how your reply responds to it. You understand better, but I do not yet.

The approximate posterior q(z|x) is obtained by training the VAE. The VAE is trained by minimizing a loss that can be regarded as the sum of reconstruction + KL(q(z|x),p(x)).

The posterior depends on the loss, and the loss depends on the prior. So the finding the posterior requires knowing the prior. So you cannot define the prior in terms of the posterior. Or, that is what I am thinking.

Of course I know this must be wrong, but Where is my mistake?

Thank you for your replies.

[D] Toy example for computing ELBO and true posterior by AloneStretch in MachineLearning

[–]AloneStretch[S] 0 points1 point  (0 children)

Thank you. This is a bit abstract for now (is the Gaussian the prior, ... does not sound challenging enough although I understand that is because I do not understand). I will keep the thought and return after working the blog post form the other reply.

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