Ontolearn is an open-source software library for explainable structured machine learning in Python. It learns OWL class expressions from positive and negative examples.
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Updated
Jul 18, 2024 - Python
Ontolearn is an open-source software library for explainable structured machine learning in Python. It learns OWL class expressions from positive and negative examples.
Bayesian framework for concept learning
Learning to Infer Generative Template Programs for Visual Concepts -- ICML 2024
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.
Implement Find-S algorithm which is used in concept learning
Concept length prediction for the ALC description logic.
EvoLearner: Learning Description Logics with Evolutionary Algorithms
[AAAI 2024] ConceptBed Evaluations for Personalized Text-to-Image Diffusion Models
Official implementation of ICLR 2023 paper "A Minimalist Dataset for Systematic Generalization of Perception, Syntax, and Semantics"
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
A concurrent implementation of the candidate elimination algorithm.
Library for hierarchical concept composition and reasoning
A novel approach to learning concept embeddings and approximate reasoning in ALC knowledge bases with deep neural networks
Some of the most popular Machine Learning Concepts.
The Codebase for Causal Proxy Model
OWL explainable structural learning problem Benchmark Generator
My Concept Learning algorithms implementation.
PyTorch implementation for the Neuro-Symbolic Concept Learner (NS-CL).
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