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- deep learning - When should I use a variational autoencoder as opposed . . .
To tackle this problem, the variational autoencoder was created by adding a layer containing a mean and a standard deviation for each hidden variable in the middle layer: Then even for the same input the decoded output can vary, and the encoded and clustered inputs become smooth:
- bayesian - What are variational autoencoders and to what learning tasks . . .
Enter Variational Inference, the tool which gives Variational Autoencoders their name Variational Inference for the VAE model Variational Inference is a tool to perform approximate Bayesian Inference for very complex models It's not an overly complex tool, but my answer is already too long and I won't go into a detailed explanation of VI
- Removing noise with Variational Autoencoders - Cross Validated
$\begingroup$ @user1533286 then when using relatively small latent dimensionality and or regularization, the autoencoder should focus only on the "strong" patterns and ignore the noise (e g MINST digits vs subtle white noise) since it would be forced to use the sparse representation that would not have enough information capacity to store the noise
- Whats a mean field variational family? - Cross Validated
To elaborate on and give context to previous answers, we expand on the use of mean-field variational assumptions in machine learning The question can be decomposed into two main parts: 1) The definition of the mean-field distribution, and 2) How the mean-field distribution is optimized in variational inference Part 1:
- Difference between stochastic variational inference and variational . . .
The coordinate ascent algorithm in Figure 3 is inefficient for large data sets because we must optimize the local variational parameters for each data point before re-estimating the global variational parameters Stochastic variational inference uses stochastic optimization to fit the global variational parameters
- regression - What is the difference between Variational Inference and . . .
Historically, variational bayes has been popular in applications that involve latent variables These latent variables are treated identically to parameters in both Bayesian and variational Bayesian inference: we place a variational distribution over each latent variable just as we did parameter
- machine learning - Variational inference versus MCMC: when to choose . . .
Thus, variational inference is suited to large data sets and scenarios where we want to quickly explore many models; MCMC is suited to smaller data sets and scenarios where we happily pay a heavier computational cost for more precise samples
- Relation between variational Bayes and EM - Cross Validated
I read somewhere that Variational Bayes method is a generalization of the EM algorithm Indeed, the iterative parts of the algorithms are very similar In order to test whether the EM algorithm is a special version of the Variational Bayes, I tried the following:
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