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Friday, November 16 • 3:55pm - 4:25pm
Structured Deep Learning with Probabilistic Neural Programs

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Machine learning problems with structured output spaces, such as generating from a context-free grammar, are difficult to represent in current deep learning frameworks. These frameworks let a user specify the neural network architecture for scoring a single output, but not the output space itself, which makes structured prediction difficult to express. In this talk, we describe probabilistic neural programs, an open-source Scala library for structured deep learning that lets a user specify both the architecture and output space in a simple, elegant form. This framework lets users rapidly implement, train, and run a variety of state-of-the-art structured prediction models that would be difficult or impossible to implement with other tools. We’ll demonstrate how the framework can be used to tackle an example structured prediction problem.

Speakers
avatar for Jayant Krishnamurthy

Jayant Krishnamurthy

Semantic Machines
Jayant is a scientist whose research focuses on machine learning techniques for natural language understanding and dialogue. He develops complex structured prediction models and the infrastructure for representing them effectively. He received his Ph.D. in Computer Science from Carnegie... Read More →


Friday November 16, 2018 3:55pm - 4:25pm
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Attendees (19)