A `Neural = Symbolic` framework for sound and complete weighted real-value logic

Overview

Build Status License

Logical Neural Networks

LNNs are a novel Neuro = symbolic framework designed to seamlessly provide key properties of both neural nets (learning) and symbolic logic (knowledge and reasoning).

  • Every neuron has a meaning as a component of a formula in a weighted real-valued logic, yielding a highly interpretable disentangled representation.
  • Inference is omnidirectional rather than focused on predefined target variables, and corresponds to logical reasoning, including classical first-order logic (FOL) theorem proving as a special case.
  • The model is end-to-end differentiable, and learning minimizes a novel loss function capturing logical contradiction, yielding resilience to inconsistent knowledge.
  • It also enables the open-world assumption by maintaining bounds on truth values which can have probabilistic semantics, yielding resilience to incomplete knowledge.

Quickstart

To install the LNN:

  1. Install GraphViz
  2. Run:
    pip install git+https://github.com/IBM/LNN.git
    

Documentation

Read the Docs Academic Papers Educational Resources Neuro-Symbolic AI API Overview Python Module
Docs Academic Papers Getting Started Neuro-Symbolic AI API Python Module

Citation

If you use Logical Neural Networks for research, please consider citing the reference paper:

@article{riegel2020logical,
  title={Logical neural networks},
  author={Riegel, Ryan and Gray, Alexander and Luus, Francois and Khan, Naweed and Makondo, Ndivhuwo and Akhalwaya, Ismail Yunus and Qian, Haifeng and Fagin, Ronald and Barahona, Francisco and Sharma, Udit and others},
  journal={arXiv preprint arXiv:2006.13155},
  year={2020}
}
Owner
International Business Machines
International Business Machines
Pretrained Cost Model for Distributed Constraint Optimization Problems

Pretrained Cost Model for Distributed Constraint Optimization Problems Requirements PyTorch 1.9.0 PyTorch Geometric 1.7.1 Directory structure baseline

2 Aug 28, 2022
Pixray is an image generation system

Pixray is an image generation system

pixray 883 Jan 07, 2023
Video Contrastive Learning with Global Context

Video Contrastive Learning with Global Context (VCLR) This is the official PyTorch implementation of our VCLR paper. Install dependencies environments

143 Dec 26, 2022
Cartoon-StyleGan2 🙃 : Fine-tuning StyleGAN2 for Cartoon Face Generation

Fine-tuning StyleGAN2 for Cartoon Face Generation

Jihye Back 520 Jan 04, 2023
PyGCL: Graph Contrastive Learning Library for PyTorch

PyGCL: Graph Contrastive Learning for PyTorch PyGCL is an open-source library for graph contrastive learning (GCL), which features modularized GCL com

GCL: Graph Contrastive Learning Library for PyTorch 594 Jan 08, 2023
We will release the code of "ConTNet: Why not use convolution and transformer at the same time?" in this repo

ConTNet Introduction ConTNet (Convlution-Tranformer Network) is proposed mainly in response to the following two issues: (1) ConvNets lack a large rec

93 Nov 08, 2022
An implementation of IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification

IMLE-Net: An Interpretable Multi-level Multi-channel Model for ECG Classification The repostiory consists of the code, results and data set links for

12 Dec 26, 2022
ADGAN - The Implementation of paper Controllable Person Image Synthesis with Attribute-Decomposed GAN

ADGAN - The Implementation of paper Controllable Person Image Synthesis with Attribute-Decomposed GAN CVPR 2020 (Oral); Pose and Appearance Attributes Transfer;

Men Yifang 400 Dec 29, 2022
Angora is a mutation-based fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without symbolic execution.

Angora Angora is a mutation-based coverage guided fuzzer. The main goal of Angora is to increase branch coverage by solving path constraints without s

833 Jan 07, 2023
This package implements the algorithms introduced in Smucler, Sapienza, and Rotnitzky (2020) to compute optimal adjustment sets in causal graphical models.

optimaladj: A library for computing optimal adjustment sets in causal graphical models This package implements the algorithms introduced in Smucler, S

Facundo Sapienza 6 Aug 04, 2022
A Pose Estimator for Dense Reconstruction with the Structured Light Illumination Sensor

Phase-SLAM A Pose Estimator for Dense Reconstruction with the Structured Light Illumination Sensor This open source is written by MATLAB Run Mode Open

Xi Zheng 14 Dec 19, 2022
🍷 Gracefully claim weekly free games and monthly content from Epic Store.

EPIC 免费人 🚀 优雅地领取 Epic 免费游戏 Introduction 👋 Epic AwesomeGamer 帮助玩家优雅地领取 Epic 免费游戏。 使用 「Epic免费人」可以实现如下需求: get:搬空游戏商店,获取所有常驻免费游戏与免费附加内容; claim:领取周免游戏及其免

571 Dec 28, 2022
A Fast and Stable GAN for Small and High Resolution Imagesets - pytorch

A Fast and Stable GAN for Small and High Resolution Imagesets - pytorch The official pytorch implementation of the paper "Towards Faster and Stabilize

Bingchen Liu 455 Jan 08, 2023
Learning embeddings for classification, retrieval and ranking.

StarSpace StarSpace is a general-purpose neural model for efficient learning of entity embeddings for solving a wide variety of problems: Learning wor

Facebook Research 3.8k Dec 22, 2022
SoK: Vehicle Orientation Representations for Deep Rotation Estimation

SoK: Vehicle Orientation Representations for Deep Rotation Estimation Raymond H. Tu, Siyuan Peng, Valdimir Leung, Richard Gao, Jerry Lan This is the o

FIRE Capital One Machine Learning of the University of Maryland 12 Oct 07, 2022
Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.

Object tracking implemented with YOLOv4, DeepSort, and TensorFlow. YOLOv4 is a state of the art algorithm that uses deep convolutional neural networks to perform object detections. We can take the ou

The AI Guy 1.1k Dec 29, 2022
A baseline code for VSPW

A baseline code for VSPW Preparation Download VSPW dataset The VSPW dataset with extracted frames and masks is available here.

28 Aug 22, 2022
Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation (ICCV 2021)

Orthogonal Jacobian Regularization for Unsupervised Disentanglement in Image Generation Home | PyTorch BigGAN Discovery | TensorFlow ProGAN Regulariza

Yuxiang Wei 54 Dec 30, 2022
Code for the paper Task Agnostic Morphology Evolution.

Task-Agnostic Morphology Optimization This repository contains code for the paper Task-Agnostic Morphology Evolution by Donald (Joey) Hejna, Pieter Ab

Joey Hejna 18 Aug 04, 2022
Spectralformer: Rethinking hyperspectral image classification with transformers

Spectralformer: Rethinking hyperspectral image classification with transformers Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza

Danfeng Hong 102 Dec 29, 2022