GPflow: A Gaussian process library using TensorFlow
This provides a tool for researchers and practitioners in machine learning to implement Gaussian processes more efficiently, though it is incremental as it builds on existing TensorFlow infrastructure.
The authors developed GPflow, a Gaussian process library using TensorFlow for core computations and Python for the front end, which leverages variational inference, automatic differentiation, and GPU hardware to enhance performance and usability.
GPflow is a Gaussian process library that uses TensorFlow for its core computations and Python for its front end. The distinguishing features of GPflow are that it uses variational inference as the primary approximation method, provides concise code through the use of automatic differentiation, has been engineered with a particular emphasis on software testing and is able to exploit GPU hardware.