Variational Autoencoder

The computational systems known as cellular automata reproduce patterns of pixels by iteratively applying simple rules based loosely on the behavior of biological cells. New work extends their utility from reproducing images to generating new ones.

Every gene in the human genome exists in a variety of mutations, and some encode protein variants that cause cells to malfunction, resulting in illness. Yet which mutations are associated with disease is largely unknown.

To reconstruct the 3D world behind a set of 2D images, machine learning systems usually require a dedicated neural network for each scene. New research enables a single trained network to generate 3D reconstructions of multiple scenes.

Using a neural network to generate realistic videos takes a lot of computation. New work performs the task efficiently enough to run on a beefy personal computer.

World models, which learn a compressed representation of a dynamic environment like, say, a video game, have delivered top results in reinforcement learning. A new method makes them much smaller.

A new generative model croons like Elvis and raps like Eminem. It might even make you think you’re listening to a lost demo by the Beatles. OpenAI released Jukebox, a deep learning system that has generated thousands of songs in styles from country to metal and soul.