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

SeC: Advancing Complex Video Object Segmentation via Progressive Concept Construction

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

[npj Digital Medicine'24] Aligning Knowledge Concepts to Whole Slide Images for Precise Histopathology Image Analysis

[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 neural networks

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.

Library for hierarchical concept composition and reasoning

Implementation of FCA and Orcale-Learning for learning implication bases

Machine Learning Lab Programs in the curriculum

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.

My Concept Learning algorithms implementation.

OWL explainable structural learning problem Benchmark Generator

Some of the most popular Machine Learning Concepts.

A concurrent implementation of the candidate elimination algorithm.

Bayesian framework for concept learning

CSC3022H: Machine Learning Lab 2: Concept Learning