We are happy to announce the 4th edition of the Graph Neural Networking challenge with attractive prizes for winners !!
Title: Creating a Network Digital Twin with Real Network Data
More info: https://t.co/m8rPPrclQo
Enjoy the challenge
I'm excited to introduce our new paper on network traffic compression using graph learning methods. Our method effectively exploits spatial correlations and temporal patterns present in network traffic to outperform GZIP by 50-65% on real-world networks. https://t.co/HL2ElTt5ed
I'm thrilled to announce that our position paper “Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities” has been accepted in the pretigious IEEE Network Magazine 🎉🎉
Pre-print in @ArXiv:
https://t.co/ctU6uu82rN
#IEEE@ComSoc; #machinelearning
Ahir, 20 de juliol es va realitzar l’acte de pressa de possessió dels nous catedràtics i catedràtiques d’universitat i dels catedràtics i catedràtiques contractats.
A totes i tots us desitgem molts encerts en aquesta nova etapa.
It is my pleasure to announce the 3rd edition of the Graph Neural Networking challenge ‼️📢📢
Title: Improving Network Digital Twins through Data-centric AI
There are attractive prizes for winners 💰💰.
Challenge website 👇👇👇
https://t.co/ODvmEpGiPI
📢 Paper alert! We are delighted to announce that the latest work "Network Digital Twin: Context, Enabling Technologies and Opportunities" has been accepted for publication at #IEEE Communications Magazine. The paper is publicly available in https://t.co/tYlV5CYE0z
🔥🔥I'm happy to share that our latest paper "Network Digital Twin: Context, Enabling Technologies and Opportunities" has been accepted for publication at #IEEE Communications Magazine, a top magazine of the telecommunications field👇
I'm very happy to share our work on Deep #ReinforcementLearning for #DigitalTwin Network #Optimization. In this paper, we explore the use of Evolutionary Strategies to train a GNN for routing optimization https://t.co/iLkvdO626k
It is my pleasure to share the preprint of our work "Digital Twin Network: Opportunities and Challenges". In this paper we present the Digital Twin Network (DTN) as a key technology for efficient network operation in modern networks
#digitaltwin
https://t.co/JA9sFqfYVG
We are happy to announce that BNN is now in Medium!!
Check out the first blog posts in https://t.co/ZFjoaildb6 to learn more about IGNNITION: an #opensource framework for fast #GNN prototyping tailored to #communication#networks
Kudos to Guillermo Bernárdez Gil, José Suárez-Varela Maciá (@jsuarezvarela), Albert López Brescó, Pere Barlet and Albert Cabellos from the Barcelona Neural Networking Center, as well as the other collaborators from Huawei Technologies Co., Ltd.
Glad to announce that the paper "Is Machine Learning Ready for Traffic Engineering Optimization?" has recently been selected among the IEEE ICNP 2021 Best Paper Awards (Runner-up).
#machinelearning#graphneuralnetworks#computernetworks
In the coming weeks we will celebrate an online award ceremony of the Graph Neural Networking challenge, which will be open to the public. More updates in the next few days.
Links:
https://t.co/j0PUOdiADb
https://t.co/F4aINSzT7K
#graphneuralnetworks@ITU@ITU_AIForGood
Glad to announce the final ranking of the Graph Neural Networking challenge 2021:
https://t.co/j0PUOdiADb
Special congratulations to top-3 teams, which will access to the Grand Challenge Finale of the ITU AI/ML in 5G Challenge (https://t.co/F4aINSzT7K)
🧵👇
#machinelearning
👏👏👏
1st-PARANA
B. Klaus de Aquino Afonso
Universidade Federal de São Paulo @unifesp
2nd-SOFGNN
C. Boudreau, H. Duong, B. Jaumard, J. Momo Ziazet
Concordia University @Concordia
3rd-ZTE AIOps
H. Zhuoyao, T. Yunsheng, G. Yong, T. Qingkun
ZTE Corporation @ZTEPress
In this work, we analyze some of the main limitations of current AI solutions for Computer Networks, and propose a novel approach based on #GraphNeuralNetworks and Multi-Agent Reinforcement Learning that brings closer the deployment of AI solutions to real-world networks.
Good news from the Barcelona Neural Networking Center!
Our paper "Is Machine Learning Ready for Traffic Engineering Optimization?" was accepted at IEEE ICNP 2021.
Link to paper: https://t.co/OW4B6rEjo2
Teaser (1:30 mins) 👇
🧵👇
#machinelearning#computernetworks
Good news from the @BNN_UPC team!
We have recently presented a demo at ACM SIGCOMM:
"IGNNITION: Fast prototyping of Graph Neural Networks for Communication Networks"
Website: https://t.co/lPotJuMJYy
Paper: https://t.co/fcntDZMkom
Video: https://t.co/fKB9MAhUte
🧵👇
Glad to announce that our paper "The #GraphNeuralNetworking Challenge: A Worldwide Competition for Education in AI/ML for Networks" has been published at @ACMSIGCOMM CCR. This paper features our ML competition, co-organized with @ITU.
https://t.co/PMZ764eHeb
#MachineLearning
🧵👇
We have just finished the round-table of the Graph Neural Networking challenge, co-organized with @ITU (@UN). I must say that participants have raised many interesting questions 😊😊
Below, the slides I used:
https://t.co/i8ZYxD7LwV
Website:
https://t.co/88KO5R5ynM
Participate now in the @ITU_AIForGood#Challenge with @BNN_UPC and win up to 4.646€! Graph Neural Networks (GNN) is building the next generation internet and now is inviting you to help them actively in this endeavor!
🔥 Discover more on the Challenge!
https://t.co/kT2td9skQG