Image credit: https://tkipf.github.io/graph-convolutional-networks/
Graph Neural Networks (GNNs) have gained immense popularity as a new methodology of analysing graphs, similar to how we use deep learning to process images. GNNs can process many real‐world datasets, which inherently have graph structures. They also provide application scenarios in different domains, from recommendation systems to road traffic predictions and drug design. GNNs use the same principles as Convolutional Neural Networks (CNNs) with re‐definition for the graph domain. Like in images, the convolution framework of GNNs is capturing neighbourhood information of graph nodes to process graph structures represent in molecules, social‐, citation‐ and road networks. With the advancements of machine learning, graph data processing is nowadays possible in the most effective manner.
This repository contains the training and inference source code of a GCN for our technology platform. Please note that model training requires TensorFlow 1.15 and model inference our pyACL framework.
Please use the following link to visit our repository. http://shrnk.cc/sdrwl
We are looking for a PhD student for Arm vectorization/HPC who would like to do an internship at our Munich Research Center
We are looking for a PhD student for artificial intelligence who would like to do an internship at our Munich Research Center
To young talents and fresh graduates! Please join our fireside conversations with John Hopcroft—a pioneer of computer science and Turing award winner. This event is the first appointment of a fantastic series to explore career progression themes within computer science and artificial intelligence.
We will have an exclusive live conversion to explore the industry's future, how it will help solve global issues and unearth career inspiration for everything—whether you are a novice or veteran.
You will hear first-hand experiences, personal stories and maybe a few predictions. Please do not miss it!
2 PM-3 PM CET on April 22, 2021
Sign up link: https://bit.ly/2PO0Cg1
We just published our 2020 annual report and can announce that the numbers are in line with the marginal growth we forecasted. The sales revenue weighed in at CNY 891.4 billion ( $136.7 billion) with a net profit of CNY 64.6 billion ($9.9 billion). Despite the US sanctions set in 2019 by the Trump Administration and all the difficulties caused by the Coronavirus pandemic, our revenue is up 3.8% YoY, and profits are up 3.2% YoY.
Please use the following link for detailed informations: https://www.huawei.com/en/annual-report/2020
Over the past year, we’ve held strong in the face of adversity. We’ve kept innovating to create value for our customers, to help fight the pandemic, and to support both economic recover and social progress around the world. We also took this opportunity to further enhance our operations, leading to a performance that was largely in line with forecast.
The third episode of "100 Faces of Huawei" starts with an epic drone video footage of our new Ox Horn research and development campus. Then, it shows how 5G will interconnect cities and revolutionize our industrial world. Also, it gives insights into our smartphone manufacturing and design process. Last, it answers the question of who is holding Huawei's shares and who is controlling Huawei. Please enjoy.
In the second episode of "100 Faces of Huawei", Japanese director Takeuchi Ryō shows how we are using artificial intelligence to improve the check-in/security process of the Shenzhen airport, the work situation for tower crane operators of the port in Shenzhen and how to protect the wildlife of the Siberian tiger. It also shows how to use our 360-degree camera system to create new viewing experiences for sports events or movie scenes.
There are two sides to every coin. It is wonderful to see that Japanese director Takeuchi Ryo had the opportunity to give insights into our company, our working culture and international employees, like me.
The updated version of this presentation provides the detailed descriptions of two more projects:
The updated version of this presentation provides the following changes: