Neuro Symbolic
Neuro-symbolic AI integrates neural networks' learning capabilities with symbolic AI's reasoning and explainability, aiming to create more robust, interpretable, and efficient AI systems. Current research focuses on developing hybrid models that combine neural networks (e.g., transformers, graph neural networks) with symbolic reasoning frameworks (e.g., logic tensor networks, logic programming), often applied to tasks like planning, question answering, and knowledge graph reasoning. This approach addresses limitations of purely neural or symbolic methods, offering potential for improved performance and trustworthiness in various applications, including robotics, natural language processing, and knowledge representation.
170papers
Papers - Page 3
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VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning
Yichao Liang, Nishanth Kumar, Hao Tang, Adrian Weller, Joshua B. Tenenbaum, Tom Silver, João F. Henriques, Kevin EllisA Walsh Hadamard Derived Linear Vector Symbolic Architecture
Mohammad Mahmudul Alam, Alexander Oberle, Edward Raff, Stella Biderman, Tim Oates, James Holt
October 29, 2024
NeuroSym-BioCAT: Leveraging Neuro-Symbolic Methods for Biomedical Scholarly Document Categorization and Question Answering
Parvez Zamil, Gollam Rabby, Md. Sadekur Rahman, Sören AuerMapping the Neuro-Symbolic AI Landscape by Architectures: A Handbook on Augmenting Deep Learning Through Symbolic Reasoning
Jonathan Feldstein, Paulius Dilkas, Vaishak Belle, Efthymia Tsamoura
October 20, 2024
Integrating Symbolic Neural Networks with Building Physics: A Study and Proposal
Xia Chen, Guoquan Lv, Xinwei Zhuang, Carlos Duarte, Stefano Schiavon, Philipp GeyerA Remedy to Compute-in-Memory with Dynamic Random Access Memory: 1FeFET-1C Technology for Neuro-Symbolic AI
Xunzhao Yin, Hamza Errahmouni Barkam, Franz Müller, Yuxiao Jiang, Mohsen Imani, Sukhrob Abdulazhanov, Alptekin Vardar, Nellie Laleni+9
October 18, 2024
October 5, 2024