Neuro-Symbolic Learning

Neuro-symbolic reasoning represents a significant advancement in the field of artificial intelligence, merging the intuitive pattern recognition capabilities of neural networks with the logical and interpretable framework of symbolic AI. This hybrid approach addresses critical limitations of both approaches by combining their respective strengths. By combining the learning efficiency and adaptability of neural networks with the explicit reasoning and rule-based processing of symbolic AI, neuro-symbolic systems can achieve more accurate, transparent, flexible, and reliable decision-making. This integration is especially crucial in applications requiring both data-driven insights and logical, explainable decisions, such as understanding communications, recognizing objects, and robotics. Neuro-symbolic reasoning thus paves the way for more robust, understandable, and trustworthy AI systems, aligning machine intelligence more closely with human-like understanding and reasoning while simultaneously providing a way of post-training adaption through the alteration of the symbolic knowledge.

• Publication

  Geometric Shape Reasoning for Zero-Shot Task-Oriented Grasping
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  Symbolic Graph Inference for Compound Scene Understanding
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  Transfer Learning via Temporal Contrastive Learning
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  HiKER-SGG: Hierarchical Knowledge Enhanced Robust Scene Graph Generation
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  Long-Horizon Dialogue Understanding for Role Identification in the Game of Avalon with Large Language Models
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  Hiker-sgg: Hierarchical knowledge enhanced robust scene graph generation
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  Knowledge-guided short-context action anticipation in human-centric videos
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  Sample-efficient learning of novel visual concepts
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  Learning Modular Language-Conditioned Robot Policies Through Attention
• Projects

  Neuro-Symbolic Learning

neuro-symbolic