PyTorch implementation for the Neuro-Symbolic Concept Learner (NS-CL).
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Updated
Oct 24, 2020 - Python
PyTorch implementation for the Neuro-Symbolic Concept Learner (NS-CL).
Ontolearn is an open-source software library for explainable structured machine learning in Python. It learns OWL class expressions from positive and negative examples.
ZeroC is a neuro-symbolic method that trained with elementary visual concepts and relations, can zero-shot recognize and acquire more complex, hierarchical concepts, even across domains
[AAAI 2024] ConceptBed Evaluations for Personalized Text-to-Image Diffusion Models
A novel approach to learning concept embeddings and approximate reasoning in ALC knowledge bases with deep neural networks
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Official implementation of ICLR 2023 paper "A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics"
Implementation of FCA and Orcale-Learning for learning implication bases
Learning to Infer Generative Template Programs for Visual Concepts -- ICML 2024
Library for hierarchical concept composition and reasoning
OntoSample is a python package that offers classic sampling techniques for OWL ontologies/knowledge bases. Furthermore, we have tailored the classic sampling techniques to the setting of concept learning making use of learning problem.
EvoLearner: Learning Description Logics with Evolutionary Algorithms
My Concept Learning algorithms implementation.
Concept length prediction for the ALC description logic.
Implement Find-S algorithm which is used in concept learning
OWL explainable structural learning problem Benchmark Generator
Some of the most popular Machine Learning Concepts.
CSC3022H: Machine Learning Lab 2: Concept Learning
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