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Found 5 papers, 2 papers with code
The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning
In this paper, we leverage the implicit knowledge within foundation models to enhance the performance in NeSy tasks, whilst reducing the amount of data labelling and manual engineering.
Neuro-Symbolic Learning of Answer Set Programs from Raw Data
A promising direction for achieving this goal is Neuro-Symbolic AI, which aims to combine the interpretability of symbolic techniques with the ability of deep learning to learn from raw data.
FF-NSL: Feed-Forward Neural-Symbolic Learner
To address this limitation, we propose a neural-symbolic learning framework, called Feed-Forward Neural-Symbolic Learner (FFNSL), that integrates a logic-based machine learning system capable of learning from noisy examples, with neural networks, in order to learn interpretable knowledge from labelled unstructured data.
NSL: Hybrid Interpretable Learning From Noisy Raw Data
Using the scoring function of FastLAS, NSL searches for short, interpretable rules that generalise over such noisy examples.
FLAP -- A Federated Learning Framework for Attribute-based Access Control Policies
The design of a policy transfer framework has challenges, including policy conflicts and privacy issues.
Cryptography and Security
