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Found 5 papers, 2 papers with code
A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making
To enable decision-making under uncertainty and partial observability, we developed a novel probabilistic neuro-symbolic framework, Probabilistic Logical Neural Networks (PLNN), which combines the capabilities of logical reasoning with probabilistic graphical models.
Proof Extraction for Logical Neural Networks
Automated Theorem Provers (ATPs) are widely used for the verification of logical statements.
Leveraging Abstract Meaning Representation for Knowledge Base Question Answering
Knowledge base question answering (KBQA)is an important task in Natural Language Processing.
Logical Neural Networks
We propose a novel framework seamlessly providing key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning).
Active Learning with TensorBoard Projector
An ML-based system for interactive labeling of image datasets is contributed in TensorBoard Projector to speed up image annotation performed by humans.
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