Neuro-symbolic AI approaches have recently begun to generate significant interest, as urgency in the field appears to be growing around various ideas for somehow extending the strengths and success of neural networks (or machine learning, more broadly) with capabilities typically found in symbolic, or classical AI (such as knowledge representation and reasoning). A general aim of this research is to create a new class of far more powerful than the sum of its parts, and leverage the best of both worlds while simultaneously addressing the shortcomings of each. Typical advantages sought include the ability to:
-Perform reasoning to solve more difficult problems
-Leverage explicit domain knowledge where available
-Learn with many fewer examples
-Provide understandable or verifiable decisions
These abilities are particularly relevant to the adoption of AI in a broader array of industrial and societal problems where data is scarce, the stakes are higher, and where the scrutability of systems is important.
This research direction is at once an old pursuit and nascent, and several perspectives are expected to be needed in order to solve this grand challenge. In this workshop we will explore several points of view, both from industry and academia, and highlight strong recent and emerging results that we believe are providing new fundamental insights for the area and also beginning to demonstrate state-of-the-art results on both the theoretical side and the applied side.
Sun 10:00 a.m. - 10:10 a.m.
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Opening Remarks
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Opening remarks
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David Cox 🔗 |
Sun 10:10 a.m. - 10:30 a.m.
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Opening Remarks & Logical Neural Networks
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Talk
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Alexander Gray 🔗 |
Sun 10:30 a.m. - 10:45 a.m.
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Real-valued reasoning
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Talk
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SlidesLive Video |
Ronald Fagin 🔗 |
Sun 10:45 a.m. - 10:55 a.m.
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Decision procedures for real valued reasoning
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Talk
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SlidesLive Video |
Ryan Riegel 🔗 |
Sun 10:55 a.m. - 11:00 a.m.
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Q/A (Logical Neural Networks, Real valued reasoning, Decision procedures for real valued reasoning)
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Q/A Session
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Sun 11:00 a.m. - 11:13 a.m.
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Project Deep Thinking: A Neuro-Symbolic approach to knowledge base question answering Parsing
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Talk
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Salim Roukos 🔗 |
Sun 11:13 a.m. - 11:26 a.m.
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State-of-the-art Question Answering via a Neuro-symbolic Approach
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Talk
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SlidesLive Video |
Pavan Kapanipathi 🔗 |
Sun 11:26 a.m. - 11:30 a.m.
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Q/A (Project Deep Thinking and State-of-the-art Question Answering)
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Q/A Session
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Sun 11:30 a.m. - 12:05 p.m.
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Doing for our robots what nature did for us
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Talk
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Leslie Kaelbling 🔗 |
Sun 12:05 p.m. - 12:10 p.m.
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Q/A (Doing for our robots what nature did for us)
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Q/A Session
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Sun 12:10 p.m. - 12:25 p.m.
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Neuro-Symbolic Visual Concept Learning
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Talk
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Jiajun Wu 🔗 |
Sun 12:25 p.m. - 12:30 p.m.
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Q/A (Neuro-Symbolic Visual Concept Learning)
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Q/A Session
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Sun 12:30 p.m. - 12:45 p.m.
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Combining Bayesian, neural network and symbolic approach to intuitive physics
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Talk
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Akash Srivastava 🔗 |
Sun 12:45 p.m. - 12:50 p.m.
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Q/A (Combining Bayesian, neural network and symbolic approach to intuitive physics)
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Q/A Session
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Sun 12:50 p.m. - 1:05 p.m.
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Neurosymbolic Visual Reasoning
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Talk
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SlidesLive Video |
Chuang Gan 🔗 |
Sun 1:05 p.m. - 1:10 p.m.
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Q/A (Neurosymbolic Visual Reasoning)
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Q/A Session
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Sun 1:10 p.m. - 1:25 p.m.
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TRAIL: Reinforcement Learning based Theorem Proving
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Talk
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Achille Fokoue 🔗 |
Sun 1:25 p.m. - 1:30 p.m.
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Q/A (TRAIL: Reinforcement Learning Based Theorem Proving)
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Q/A Session
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Sun 1:30 p.m. - 1:45 p.m.
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Challenges for Compositional Generalization
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Talk
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Tim Klinger 🔗 |
Sun 1:45 p.m. - 1:50 p.m.
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Q/A (Challenges for Compositional Generalization)
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Q/A Session
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Sun 1:50 p.m. - 1:55 p.m.
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Closing Remarks
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Closing Remarks
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David Cox · Alexander Gray 🔗 |