Vertex-Enriched Graph Neural Network (VEGNN)
-
Updated
Jun 2, 2021 - Jupyter Notebook
Vertex-Enriched Graph Neural Network (VEGNN)
BotGNN: Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment
Master's thesis : Knowledge Inference and Knowledge Completion Methods using Neuro-Symbolic Inductive Rules
Code for "ELLEN: Extremely Lightly Supervised Learning For Efficient Named Entity Recognition" (LREC-COLING 2024)
The official repository for the PSYCHIC model
Implementation of a straight-through gradient wrapper to allow for discrete latent representations. Provides binary discretizer which maps hidden representations to {0, 1} and a learnable multi-value discretizer, which maps hidden activations to their closest value in a set of given size.
An attempt to merge ESBN with Transformers, to endow Transformers with the ability to emergently bind symbols
An efficient Python toolkit for Abductive Learning (ABL), a novel paradigm that integrates machine learning and logical reasoning in a unified framework.
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 deep neural networks
Usable implementation of Emerging Symbol Binding Network (ESBN), in Pytorch
Tree Stack Memory Units
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.
Neuro-Symbolic Visual Question Answering on Sort-of-CLEVR using PyTorch
Python library that enables using prolog syntax and logic programming in python
AIKA is a new type of artificial neural network designed to more closely mimic the behavior of a biological brain and to bridge the gap to classical AI. A key design decision in the Aika network is to conceptually separate the activations from their neurons, meaning that there are two separate graphs. One graph consisting of neurons and synapses…
A collection of papers of neural-symbolic AI (mainly focus on NLP applications)
Implementation for the Neural Logic Machines (NLM).
Add a description, image, and links to the neuro-symbolic-learning topic page so that developers can more easily learn about it.
To associate your repository with the neuro-symbolic-learning topic, visit your repo's landing page and select "manage topics."