Bayesian probability

Physics

Machine learning

Gaussian

Artificial intelligence

Computer science

Probabilistic logic

Gaussian process

Quantum mechanics

Bayesian network

Scalability

Artificial neural network

Database

arXiv (Cornell University)

We describe Bayesian Layers, a module designed for fast experimentation with neural network uncertainty. It extends neural network libraries with drop-in replacements for common layers. This enables composition via a unified abstraction over deterministic and stochastic functions and allows for scalability via the underlying system. These layers capture uncertainty over weights (Bayesian neural nets), pre-activation units (dropout), activations (``stochastic output layers''), or the function itself (Gaussian processes). They can also be reversible to propagate uncertainty from input to output. We include code examples for common architectures such as Bayesian LSTMs, deep GPs, and flow-based models. As demonstration, we fit a 5-billion parameter ``Bayesian Transformer'' on 512 TPUv2 cores for uncertainty in machine translation and a Bayesian dynamics model for model-based planning. Finally, we show how Bayesian Layers can be used within the Edward2 language for probabilistic programming with stochastic processes.

Bayesian Layers: A Module for Neural Network Uncertainty