Neuro-Symbolic AI with Knowledge Graph | Reasoning thru AI Language 🌱🐋🌍

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

Implementation for the Neural Logic Machines (NLM).

A collection of papers of neural-symbolic AI (mainly focus on NLP applications)

[CVPR 2024] Neural Markov Random Field for Stereo Matching

Python library that enables using prolog syntax and logic programming in python

An efficient Python toolkit for Abductive Learning (ABL), a novel paradigm that integrates machine learning and logical reasoning in a unified framework.

AIKA (Artificial Intelligence for Knowledge Acquisition) is an innovative approach to neural network design, diverging from traditional architectures that rely heavily on rigid matrix and vector operations. The AIKA Project introduces a flexible, sparse, and non-layered network representation, derived from a type hierarchy.

Neuro-Symbolic Visual Question Answering on Sort-of-CLEVR using PyTorch

Holographic Reduced Representations

RelNN is a novel first-order deep neural model for relational learning.

Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.

Tree Stack Memory Units

Pytorch implementation for Perspective Plane Program Induction from a Single Image (P3I).

A novel approach to learning concept embeddings and approximate reasoning in ALC knowledge bases with neural networks

BotGNN: Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment

PyEDCR is a metacognitive neuro-symbolic method for learning error detection and correction rules in deployed ML models using combinatorial sub-modular set optimization

Vertex-Enriched Graph Neural Network (VEGNN)

The official repository for the PSYCHIC model

Code for the paper "ELLEN: Extremely Lightly Supervised Learning For Efficient Named Entity Recognition" (LREC-COLING 2024)