We present high quality image synthesis results using diffusion probabilistic models, a class
of latent variable models inspired by considerations from nonequilibrium thermodynamics …

Neural compression is the application of neural networks and other machine learning
methods to data compression. Recent advances in statistical machine learning have opened …

Communication complexity is the mathematical study of scenarios where several parties
need to communicate to achieve a common goal, a situation that naturally appears during …

The concept of free energy has its origins in 19th century thermodynamics, but has recently
found its way into the behavioral and neural sciences, where it has been promoted for its …

The problem of constructing optimal and near-optimal test sequences to diagnose
permanent faults in electronic and electromechanical systems is considered. The test …

Relative entropy coding (REC) algorithms encode a sample from a target distribution $ Q $
using a proposal distribution $ P $ using as few bits as possible. Unlike entropy coding, REC …

Two familiar notions of correlation are rediscovered as the extreme operating points for
distributed synthesis of a discrete memoryless channel, in which a stochastic channel output …

We describe new ways to simulate 2-party communication protocols to get protocols with
potentially smaller communication. We show that every communication protocol that …

A popular approach to learning encoders for lossy compression is to use additive uniform
noise during training as a differentiable approximation to test-time quantization. We …

We show how to efficiently simulate the sending of a single message M to a receiver who
has partial information about the message, so that the expected number of bits …