Efficient Automatic Differentiation of Higher-Order Functions

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Researchers at Purdue University have developed methods of Automatic Differentiation (AD) to be applied to both rigid computations and arbitrary computer programs. This technology greatly increases the efficiency of these processes while also reducing the amount of required computer memory, allowing for more complicated deep learning systems.


Potential Applications:
-Application Programmers
-Machine Learning
Apr 29, 2020
PCT-Gov. Funding

Apr 29, 2019
United States
Purdue Office of Technology Commercialization
The Convergence Center
101 Foundry Drive, Suite 2500
West Lafayette, IN 47906

Phone: (765) 588-3475
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Email: otcip@prf.org