猫でも分かるVariational AutoEncoder
1. The document discusses probabilistic modeling and variational inference. It introduces concepts like Bayes' rule, marginalization, and conditioning. 2. An equation for the evidence lower bound is derived, which decomposes the log likelihood of data into the Kullback-Leibler divergence between an approximate and true posterior plus an expected log likelihood term. 3. Variational autoencoders are
How to Escape Saddle Points Efficiently
This paper shows that a perturbed form of gradient descent converges to a second-order stationary point in a number iterations which depends only poly-logarithmically on dimension (i.e., it is almost "dimension-free"). The convergence rate of this procedure matches the well-known convergence rate of gradient descent to first-order stationary points, up to log factors. When all saddle points are no
Building Autoencoders in Keras
In this tutorial, we will answer some common questions about autoencoders, and we will cover code examples of the following models: a simple autoencoder based on a fully-connected layer a sparse autoencoder a deep fully-connected autoencoder a deep convolutional autoencoder an image denoising model a sequence-to-sequence autoencoder a variational autoencoder Note: all code examples have been updat
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http://approximateinference.org/accepted/RoederEtAl2016.pdf
Variational Tempering
https://keras.io/getting-started/functional-api-guide/
