Meta AI Open-Sources Theseus, A Library For Differential Nonlinear Optimization
Researchers at Meta AI have developed a new PyTorch library called Theseus. This library lets developers implement differentiable nonlinear optimization into any model architecture and application.
Created on July 20|Last edited on July 20
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Meta AI researchers have developed an open-source PyTorch library called Theseus which aims to support programmers in developing models with end-to-end differentiable architectures by introducing powerful and efficient custom nonlinear optimization layers.
Optimization is a key part of efficient deep learning, as it helps guide the model towards success in a variety of different ways. Theseus is able to integrate differential nonlinear optimization into fully end-to-end model architectures with the introduction of "theseus" layers. These layers can be inserted into any model architecture, for any model application, bringing this powerful optimization tactic into any project.
Learn more about Theseus
Read the official blog post about Theseus' release to understand more about what it's doing and how it works.
Theseus was released open-source today, and you can find all the code for the project at their GitHub repository here: https://github.com/facebookresearch/theseus. There are also tutorials and examples for those who are ready to get right into it. You can also find more guidance at the documentation site.
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