Efficient Automatic Differentiation of Higher-Order Functions

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2019-SISK-68524
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.

Advantages:
-Efficient
-Versatile

Potential Applications:
-Application Programmers
-Machine Learning
-AI
Apr 29, 2020
PCT-Gov. Funding
WO
(None)
(None)

Apr 29, 2019
Provisional-Patent
United States
(None)
(None)
Purdue Office of Technology Commercialization
1801 Newman Road
West Lafayette, IN 47906

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