Since the early days of machine learning, it has been attempted to learn good representations of data in an unsupervised manner. The hypothesis underlying this effort is that disentangled representations translate well to downstream supervised tasks. For example, if a human is told that a Tesla is a car and he has a good representation of what a car looks like, he can probably … [Read more...] about Autoencoders: Overview of Research and Applications
Variational Methods in Deep Learning
Deep neural networks are a flexible family of models wide applications in AI and other fields. Even though these networks often encompass millions or even billions of parameters, it is still possible to train them effectively using the maximum likelihood principle as well as stochastic gradient descent techniques. Unfortunately, this learning procedure only gives us a … [Read more...] about Variational Methods in Deep Learning