Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data

Overview

VIMuRe

Github Badge codecov

Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data.

If you use this code please cite this article (preprint).

De Bacco C, Contisciani M, Cardoso-Silva J, Safdari H, Baptista D, Sweet T, Young JG, Koster J, Ross CT, McElreath R, Redhead D. Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data. arXiv preprint arXiv:2112.11396. 2021.

Notebooks

To understand how you could use the code or to replicate our research, check the notebooks folder:

Setup

Instructions on how to replicate our research either downloading our package via pip or by using the Docker setup used for developing the package.

Alternative 01: using pip

Install vimure==0.1 with the following command:

pip install git+https://github.com/latentnetworks/vimure.git#egg=vimure\&subdirectory=src/python/

Alternative 02: using Docker

If you are familiar with Docker, you could use the Docker containers provided in this repository. Use this setup if you want to modify anything in the package.

  1. Clone the repository to a directory in your machine
  2. Install Docker and docker-compose on your machine
  3. Open the terminal and build the project:
cd vimure
docker-compose build

The first time you run this build, it will take several minutes to complete. Trust me, it is better to run this and wait the building time than having to install each multiple python dependencies by hand and having to figure out why your colleague gets a weird and mysterious, previously unseen Exception when running the same code as you!

  1. Still on the terminal, run Jupyter server with the command:
docker-compose up

A URL will show up on your screen, either click on it or copy-paste to your browser and run the notebooks.

License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NON INFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Official implementation of the paper 'Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution'

DASR Paper Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution Jie Liang, Hui Zeng, and Lei Zhang. In arxiv preprint. Abs

81 Dec 28, 2022
Recurrent Scale Approximation (RSA) for Object Detection

Recurrent Scale Approximation (RSA) for Object Detection Codebase for Recurrent Scale Approximation for Object Detection in CNN published at ICCV 2017

Yu Liu (Louis) 239 Dec 28, 2022
A more easy-to-use implementation of KPConv based on PyTorch.

A more easy-to-use implementation of KPConv This repo contains a more easy-to-use implementation of KPConv based on PyTorch. Introduction KPConv is a

Zheng Qin 36 Dec 29, 2022
Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021)

Investigating Attention Mechanism in 3D Point Cloud Object Detection (arXiv 2021) This repository is for the following paper: "Investigating Attention

52 Nov 19, 2022
🚀 PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)"

PyTorch Implementation of "Progressive Distillation for Fast Sampling of Diffusion Models(v-diffusion)" Unofficial PyTorch Implementation of Progressi

Vitaliy Hramchenko 58 Dec 19, 2022
Visualizing Yolov5's layers using GradCam

YOLO-V5 GRADCAM I constantly desired to know to which part of an object the object-detection models pay more attention. So I searched for it, but I di

Pooya Mohammadi Kazaj 200 Jan 01, 2023
Model Quantization Benchmark

Introduction MQBench is an open-source model quantization toolkit based on PyTorch fx. The envision of MQBench is to provide: SOTA Algorithms. With MQ

500 Jan 06, 2023
Catbird is an open source paraphrase generation toolkit based on PyTorch.

Catbird is an open source paraphrase generation toolkit based on PyTorch. Quick Start Requirements and Installation The project is based on PyTorch 1.

Afonso Salgado de Sousa 5 Dec 15, 2022
The final project of "Applying AI to 3D Medical Imaging Data" from "AI for Healthcare" nanodegree - Udacity.

Quantifying Hippocampus Volume for Alzheimer's Progression Background Alzheimer's disease (AD) is a progressive neurodegenerative disorder that result

Omar Laham 1 Jan 14, 2022
Code and experiments for "Deep Neural Networks for Rank Consistent Ordinal Regression based on Conditional Probabilities"

corn-ordinal-neuralnet This repository contains the orginal model code and experiment logs for the paper "Deep Neural Networks for Rank Consistent Ord

Raschka Research Group 14 Dec 27, 2022
PyTorch implementation for ACL 2021 paper "Maria: A Visual Experience Powered Conversational Agent".

Maria: A Visual Experience Powered Conversational Agent This repository is the Pytorch implementation of our paper "Maria: A Visual Experience Powered

Jokie 22 Dec 12, 2022
Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation

Identifying a Training-Set Attack’s Target Using Renormalized Influence Estimation By: Zayd Hammoudeh and Daniel Lowd Paper: Arxiv Preprint Coming soo

Zayd Hammoudeh 2 Oct 08, 2022
Transferable Unrestricted Attacks, which won 1st place in CVPR’21 Security AI Challenger: Unrestricted Adversarial Attacks on ImageNet.

Transferable Unrestricted Adversarial Examples This is the PyTorch implementation of the Arxiv paper: Towards Transferable Unrestricted Adversarial Ex

equation 16 Dec 29, 2022
Implementation of "A MLP-like Architecture for Dense Prediction"

A MLP-like Architecture for Dense Prediction (arXiv) Updates (22/07/2021) Initial release. Model Zoo We provide CycleMLP models pretrained on ImageNet

Shoufa Chen 244 Dec 27, 2022
Code for the paper "Jukebox: A Generative Model for Music"

Status: Archive (code is provided as-is, no updates expected) Jukebox Code for "Jukebox: A Generative Model for Music" Paper Blog Explorer Colab Insta

OpenAI 6k Jan 02, 2023
An experimental technique for efficiently exploring neural architectures.

SMASH: One-Shot Model Architecture Search through HyperNetworks An experimental technique for efficiently exploring neural architectures. This reposit

Andy Brock 478 Aug 04, 2022
Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset

Lighting the Darkness in the Deep Learning Era: A Survey, An Online Platform, A New Dataset This repository provides a unified online platform, LoLi-P

Chongyi Li 457 Jan 03, 2023
A Fast Knowledge Distillation Framework for Visual Recognition

FKD: A Fast Knowledge Distillation Framework for Visual Recognition Official PyTorch implementation of paper A Fast Knowledge Distillation Framework f

Zhiqiang Shen 129 Dec 24, 2022
Dense matching library based on PyTorch

Dense Matching A general dense matching library based on PyTorch. For any questions, issues or recommendations, please contact Prune at

Prune Truong 399 Dec 28, 2022
Cereal box identification in store shelves using computer vision and a single train image per model.

Product Recognition on Store Shelves Description You can read the task description here. Report You can read and download our report here. Step A - Mu

Nicholas Baraghini 1 Jan 21, 2022