For IBM Quantum Challenge Africa 2021, 9 September (07:00 UTC) - 20 September (23:00 UTC).

Overview

IBM Quantum Challenge Africa 2021

To ensure Africa is able to apply quantum computing to solve problems relevant to the continent, the IBM Research Lab in South Africa and the University of the Witwatersrand have developed a quantum computing challenge that focuses on the fields of optimization, finance, and chemistry. This challenge will boost participant's quantum computing skills and give them the tools to devise the best solutions to real-world issues faced in Africa.

The challenge exercises are developed by African researchers for African learners, researchers, and industry professionals. Participants need not have any formal education in quantum computing, as the challenge focuses on its application to already existing classical problems.

The challenge will take place from 9 September (07:00 UTC) to 20 September (23:00 UTC). Read more about the challenge in the announcement blog.

Make sure to join the dedicated Slack channel #challenge-africa-2021 where you can connect with mentors and fellow attendees! Join the Qiskit Slack workspace here if you haven't already. Please also review our Slack Guidelines to make the most of your experience!

Event Code of Conduct

Preliminary Content

FAQ

Submitting Solutions

Re-TACRED: Addressing Shortcomings of the TACRED Dataset

Re-TACRED Re-TACRED: Addressing Shortcomings of the TACRED Dataset

George Stoica 40 Dec 10, 2022
This program presents convolutional kernel density estimation, a method used to detect intercritical epilpetic spikes (IEDs)

Description This program presents convolutional kernel density estimation, a method used to detect intercritical epilpetic spikes (IEDs) in [Gardy et

Ludovic Gardy 0 Feb 09, 2022
The implementation for the SportsCap (IJCV 2021)

SportsCap: Monocular 3D Human Motion Capture and Fine-grained Understanding in Challenging Sports Videos ProjectPage | Paper | Video | Dataset (Part01

Chen Xin 79 Dec 16, 2022
Parameterized Explainer for Graph Neural Network

PGExplainer This is a Tensorflow implementation of the paper: Parameterized Explainer for Graph Neural Network https://arxiv.org/abs/2011.04573 NeurIP

Dongsheng Luo 89 Dec 12, 2022
PyTorchMemTracer - Depict GPU memory footprint during DNN training of PyTorch

A Memory Tracer For PyTorch OOM is a nightmare for PyTorch users. However, most

Jiarui Fang 9 Nov 14, 2022
Hydra Lightning Template for Structured Configs

Hydra Lightning Template for Structured Configs Template for creating projects with pytorch-lightning and hydra. How to use this template? Create your

Model-driven Machine Learning 4 Jul 19, 2022
a practicable framework used in Deep Learning. So far UDL only provide DCFNet implementation for the ICCV paper (Dynamic Cross Feature Fusion for Remote Sensing Pansharpening)

UDL UDL is a practicable framework used in Deep Learning (computer vision). Benchmark codes, results and models are available in UDL, please contact @

Xiao Wu 11 Sep 30, 2022
The official implementation of CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing

CSGStumpNet The official implementation of CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsing Paper | Project page

Daxuan 39 Dec 26, 2022
Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data recorded in NumPy array

shindo.py Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data stored in NumPy array Introduction Japa

RR_Inyo 3 Sep 23, 2022
Official Repository for the ICCV 2021 paper "PixelSynth: Generating a 3D-Consistent Experience from a Single Image"

PixelSynth: Generating a 3D-Consistent Experience from a Single Image (ICCV 2021) Chris Rockwell, David F. Fouhey, and Justin Johnson [Project Website

Chris Rockwell 95 Nov 22, 2022
Fully Adaptive Bayesian Algorithm for Data Analysis (FABADA) is a new approach of noise reduction methods. In this repository is shown the package developed for this new method based on \citepaper.

Fully Adaptive Bayesian Algorithm for Data Analysis FABADA FABADA is a novel non-parametric noise reduction technique which arise from the point of vi

18 Oct 20, 2022
《A-CNN: Annularly Convolutional Neural Networks on Point Clouds》(2019)

A-CNN: Annularly Convolutional Neural Networks on Point Clouds Created by Artem Komarichev, Zichun Zhong, Jing Hua from Department of Computer Science

Artёm Komarichev 44 Feb 24, 2022
Implementation of Online Label Smoothing in PyTorch

Online Label Smoothing Pytorch implementation of Online Label Smoothing (OLS) presented in Delving Deep into Label Smoothing. Introduction As the abst

83 Dec 14, 2022
An LSTM based GAN for Human motion synthesis

GAN-motion-Prediction An LSTM based GAN for motion synthesis has a few issues reading H3.6M data from A.Jain et al , will fix soon. Prediction of the

Amogh Adishesha 9 Jun 17, 2022
A high-performance distributed deep learning system targeting large-scale and automated distributed training.

HETU Documentation | Examples Hetu is a high-performance distributed deep learning system targeting trillions of parameters DL model training, develop

DAIR Lab 150 Dec 21, 2022
Official repository for HOTR: End-to-End Human-Object Interaction Detection with Transformers (CVPR'21, Oral Presentation)

Official PyTorch Implementation for HOTR: End-to-End Human-Object Interaction Detection with Transformers (CVPR'2021, Oral Presentation) HOTR: End-to-

Kakao Brain 114 Nov 28, 2022
[Preprint] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Bag of Tricks for Training Deeper Graph Neural Networks: A Comprehensive Benchmark Study Codes for [Preprint] Bag of Tricks for Training Deeper Graph

VITA 101 Dec 29, 2022
A PyTorch implementation of the architecture of Mask RCNN

EDIT (AS OF 4th NOVEMBER 2019): This implementation has multiple errors and as of the date 4th, November 2019 is insufficient to be utilized as a reso

Sai Himal Allu 975 Dec 30, 2022
Cross-Modal Contrastive Learning for Text-to-Image Generation

Cross-Modal Contrastive Learning for Text-to-Image Generation This repository hosts the open source JAX implementation of XMC-GAN. Setup instructions

Google Research 94 Nov 12, 2022
Model Serving Made Easy

The easiest way to build Machine Learning APIs BentoML makes moving trained ML models to production easy: Package models trained with any ML framework

BentoML 4.4k Jan 08, 2023