System for Medical Concept Extraction and Linking

[ECCV 2024 Oral] ConceptExpress: Harnessing Diffusion Models for Single-image Unsupervised Concept Extraction

CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph.

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Code for the paper: Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery. ECCV 2024.

Explainability of Deep Learning Models

CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph.

CME: Concept-based Model Extraction

create concept map from textbook data

Combining Energy-Based Modeling and RL to solve the challenging Abstract Reasoning Corpus[1] tasks.

Simple spaCy-based concept extraction API, involving a dictionary of relevant concepts.

Retrospective Extraction of Visual and Logical Insights for Ontology-based interpretation of Neural Networks

ConExion

A toolkit to do concept expansion via search engine snippet

Web Of Things Ontology Inspection

Clinical text-mining/machine learning project I did as part of my masters thesis at LIG.

REST-API for LearningMiner.

The AI Examiner System you've described is clearly centered on leveraging the reasoning and structured output capabilities of Large Language Models (LLMs) for a robust academic assessment tool.

The proposed system provides high-quality augmentations for educational texts like concept definitions, applications, equations, and examples for improved user understanding.