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Found 5 papers, 4 papers with code
Neuro-symbolic Rule Learning in Real-world Classification Tasks
Neuro-symbolic rule learning has attracted lots of attention as it offers better interpretability than pure neural models and scales better than symbolic rule learning.
pix2rule: End-to-end Neuro-symbolic Rule Learning
Humans have the ability to seamlessly combine low-level visual input with high-level symbolic reasoning often in the form of recognising objects, learning relations between them and applying rules.
HySTER: A Hybrid Spatio-Temporal Event Reasoner
The task of Video Question Answering (VideoQA) consists in answering natural language questions about a video and serves as a proxy to evaluate the performance of a model in scene sequence understanding.
Learning Invariants through Soft Unification
The core characteristic of our architecture is soft unification between examples that enables the network to generalise parts of the input into variables, thereby learning invariants.
DeepLogic: Towards End-to-End Differentiable Logical Reasoning
Combining machine learning with logic-based expert systems in order to get the best of both worlds are becoming increasingly popular.
