PyTorch implementation for the Neuro-Symbolic Concept Learner (NS-CL).

OWL Class Expressions Learning in Python

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

The Codebase for Causal Proxy Model

Learning to Infer Generative Template Programs for Visual Concepts -- ICML 2024

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

Library for hierarchical concept composition and reasoning

EvoLearner: Learning Description Logics with Evolutionary Algorithms

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.

EDGE, "Evaluation of Diverse Knowledge Graph Explanations", is a framework to benchmark diverse explanations (e.g., subgraph vs logical) for node classification in knowledge graphs.

Machine Learning Lab Programs in the curriculum

My Concept Learning algorithms implementation.

Implement Find-S algorithm which is used in concept learning

Concept length prediction for the ALC description logic.

Some of the most popular Machine Learning Concepts.

OWL explainable structural learning problem Benchmark Generator