Adversarial Neural Cryptography
In 2016, researchers from Google Brain published a paper showing how neural networks can learn symmetric encryption to protect information from other AI attackers. (Read more...)
In 2016, researchers from Google Brain published a paper showing how neural networks can learn symmetric encryption to protect information from other AI attackers. (Read more...)
The encoding vector of a Conditional Variational Autoencoder (CVAE) is comprised of two components - the style encoding, and the conditioning vector. This visualization explores (Read more...)
We saw in Intro to Autoencoders how the encoding space was a non-convex manifold, which makes basic autoencoders a poor choice for generative models. Variational autoencoders fix this issue by ensuring (Read more...)
Solving problems using machine learning often involves looking at data with thousands of features. It is often possible to reduce the number of features, which speeds up training and is also useful for data visualization. One sophisiticated way to do so is using autoencoders (Read more...)
RSA is one of the first public-key cryptosystems, whose security relies on the conjectured intractability of the factoring problem. It was designed in 1977 by (Read more...)