Explore the experimental evaluation of Differentiable Inductive Logic Programming for Structured Examples in this 12-minute video from the Neuro Symbolic Channel. Delve into the intersection of symbolic methods and deep learning as presented in the AI course at Arizona State University. Examine the latest algorithms and progress towards artificial general intelligence (AGI) through the analysis of Shindo et al.'s AAAI 2021 paper. Access accompanying slides and the full research paper for a comprehensive understanding of how neural networks can learn logical relationships. Join presenters Jesse Jing, Divyagna Bavikadi, and Kaustuv Mukherji from ASU as they break down this cutting-edge approach in neuro-symbolic AI.
Learning Logical Relationships with Neural Networks - Part 3
Overview
Syllabus
(Pt. 3) How can you learn logical relationships with a neural network?
Taught by
Neuro Symbolic
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Training Logical Neural Networks via Primal-Dual Methods - Part 1
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Differentiable Inductive Logic Programming - Part 6: Q&A
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NeurASP: Experiments and Evaluation - Part 3
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Introduction to Neural Networks
