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Found 8 papers, 4 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.
Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?
We hypothesise that this occurs when concept annotations are inaccurate or how input features should relate to concepts is unclear.
Symbolic Learning for Material Discovery
Discovering new materials is essential to solve challenges in climate change, sustainability and healthcare.
Towards a Deeper Understanding of Concept Bottleneck Models Through End-to-End Explanation
Concept Bottleneck Models (CBMs) first map raw input(s) to a vector of human-defined concepts, before using this vector to predict a final classification.
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.
Synthetic Ground Truth Generation for Evaluating Generative Policy Models
Generative Policy-based Models aim to enable a coalition of systems, be they devices or services to adapt according to contextual changes such as environmental factors, user preferences and different tasks whilst adhering to various constraints and regulations as directed by a managing party or the collective vision of the coalition.
