Many algorithms in machine learning, computer vision, physical simulation, and other fields require the calculation of gradients and other derivatives. Manual derivation of gradients can be time consuming and error-prone. Automatic differentiation comprises a set of techniques to calculate the derivative of a numerical computation expressed as a computer program. These techniques are commonly used in atmospheric sciences and computational fluid dynamics, and have more recently also been adopted by machine learning researchers.

Practitioners across many fields have built a wide set of automatic differentiation tools, using different programming languages, computational primitives and intermediate compiler representations. Each of these choices comes with positive and negative trade-offs, in terms of their usability, flexibility and performance in specific domains.

This workshop will bring together researchers in the fields of automatic differentiation and machine learning to discuss ways in which advanced automatic differentiation frameworks and techniques can enable more advanced machine learning models, run large-scale machine learning on accelerators with better performance, and increase the usability of machine learning frameworks for practitioners. Topics for discussion will include:

* What abstractions (languages, kernels, interfaces, instruction sets) do we need to develop advanced automatic differentiation frameworks for the machine learning ecosystem?
* What different use cases exist in machine learning, from large-scale performance-critical models to small prototypes, and how should our toolsets reflect these needs?
* What advanced techniques from the automatic differentiation literature, such as checkpointing, differentiating through iterative processes or chaotic systems, cross-country elimination, etc., could be adopted by the ML community to enable research on new models?
* How can we foster greater collaboration between the fields of machine learning and automatic differentiation?