PyTorch implementation for the Neuro-Symbolic Concept Learner (NS-CL).
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Updated
Oct 24, 2020 - Python
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)
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…
Python library that enables using prolog syntax and logic programming in python
Neuro-Symbolic Visual Question Answering on Sort-of-CLEVR using PyTorch
Neuro-Symbolic AI with Knowledge Graph | "True Reasoning" through data and logic 🌿🌱🐋🌍
Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.
[CVPR 2024] Neural Markov Random Field for Stereo Matching
A novel approach to learning concept embeddings and approximate reasoning in ALC knowledge bases with deep neural networks
RelNN is a novel first-order deep neural model for relational learning.
Holographic Reduced Representations
Pytorch implementation for Perspective Plane Program Induction from a Single Image (P3I).
Tree Stack Memory Units
Usable implementation of Emerging Symbol Binding Network (ESBN), in Pytorch
An efficient Python toolkit for Abductive Learning (ABL), a novel paradigm that integrates machine learning and logical reasoning in a unified framework.
An attempt to merge ESBN with Transformers, to endow Transformers with the ability to emergently bind symbols
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
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