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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

PYLON: A PyTorch Framework for Learning with Constraints

February 1, 2023

Authors

Kareem Ahmed

Computer Science Department, University of California, Los Angeles


Tao Li

School of Computing, College of Engineering, University of Utah


Thy Ton

Department of Computer Science, University of California, Irvine


Quan Guo

Department of Artificial Intelligence, Sichuan University


Kai-Wei Chang

Computer Science Department, University of California, Los Angeles


Parisa Kordjamshidi

Department of Computer Science and Engineering, Michigan State University


Vivek Srikumar

School of Computing, College of Engineering, University of Utah


Guy Van den Broeck

Computer Science Department, University of California, Los Angeles


Sameer Singh

Department of Computer Science, University of California, Irvine


Proceedings:

No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Demonstration Track

Downloads:

Download PDF

Abstract:

Deep learning excels at learning task information from large amounts of data, but struggles with learning from declarative high-level knowledge that can be more succinctly expressed directly. In this work, we introduce PYLON, a neuro-symbolic training framework that builds on PyTorch to augment procedurally trained models with declaratively specified knowledge. PYLON lets users programmatically specify constraints as Python functions and compiles them into a differentiable loss, thus training predictive models that fit the data whilst satisfying the specified constraints. PYLON includes both exact as well as approximate compilers to efficiently compute the loss, employing fuzzy logic, sampling methods, and circuits, ensuring scalability even to complex models and constraints. Crucially, a guiding principle in designing PYLON is the ease with which any existing deep learning codebase can be extended to learn from constraints in a few lines code: a function that expresses the constraint, and a single line to compile it into a loss. Our demo comprises of models in NLP, computer vision, logical games, and knowledge graphs that can be interactively trained using constraints as supervision.

DOI:

10.1609/aaai.v36i11.21711


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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