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Workshop
Advances in Programming Languages and Neurosymbolic Systems (AIPLANS)
Breandan Considine · Disha Shrivastava · David Yu-Tung Hui · Chin-Wei Huang · Shawn Tan · Xujie Si · Prakash Panangaden · Guy Van den Broeck · Daniel Tarlow

Tue Dec 14 05:00 AM -- 05:00 PM (PST) @ None
Event URL: https://aiplans.github.io/ »

Neural information processing systems have benefited tremendously from the availability of programming languages and frameworks for automatic differentiation (AD). Not only do NeurIPS benefit from programming languages for automatic inference but can also be considered as a language in their own right, consisting of differentiable and stochastic primitives. Combined with neural language models, these systems are increasingly capable of generating symbolic programs a human programmer might write in a high-level language. Developing neurosymbolic systems for automatic program synthesis requires insights from both statistical learning and programming languages.

AIPLANS invites all researchers working towards the same purpose in these two communities to build on common ground. Our workshop is designed to be as inclusive as possible towards researchers engaged in building programming languages and neurosymbolic systems.

Author Information

Breandan Considine (McGill University)
Disha Shrivastava (Mila, University of Montreal)
David Yu-Tung Hui (Mila)
Chin-Wei Huang (Mila)
Shawn Tan (Mila)
Xujie Si (McGill University)
Prakash Panangaden (McGill University, Montreal)
Guy Van den Broeck (UCLA)

I am an Assistant Professor and Samueli Fellow at UCLA, in the Computer Science Department, where I direct the Statistical and Relational Artificial Intelligence (StarAI) lab. My research interests are in Machine Learning (Statistical Relational Learning, Tractable Learning), Knowledge Representation and Reasoning (Graphical Models, Lifted Probabilistic Inference, Knowledge Compilation), Applications of Probabilistic Reasoning and Learning (Probabilistic Programming, Probabilistic Databases), and Artificial Intelligence in general.

Daniel Tarlow (Microsoft Research Cambridge)

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