wiewiurek @wiewiurek@qoto.org
If you've had some problems with accessing the #CAMDAchallenge data then everything should be fixed by now.
You will find the direct link at:
http://data.camda.info/contest_dataset
Topics of our challenges include: #COVID19 #NLP #Bioinformatics #CancerResearch
RT @IARAInews
Join our director @m_k_kopp at the 'b2b SoftWareDays21' event and learn about the recent developments in machine learning!
📅10.05 at 11:50 AM CET
👉Find out more: https://2021.b2bsoftwaredays.com/page-3741
#SWDays21 #ML #AI @ClaraNeppel @KHimpele @niki_popper @TheStoryDude @MartinaHartner
Local random walk is a basis for many #MachineLearning algorithms. However, there is an interesting alternative - maximum entropy random walk.
Overview from TFML by J.Duda: https://tfml.gmum.net/assets/files/2015/Duda.pdf
Application in medical imaging by Galinsky&Frank: https://par.nsf.gov/servlets/purl/10063685
RT @IARAInews
Our director Dr 𝗦𝗲𝗽𝗽 𝗛𝗼𝗰𝗵𝗿𝗲𝗶𝘁𝗲𝗿 believes that the subject of AI deserves more attention from industry leaders.
“The manufacturing industry, mechanical engineering, plant engineering, etc.— this is where AI is going to be injected”
👉:https://www.hydraulicspneumatics.com/news/article/21163159/hannover-messe-adapts-and-changes
#AI #ML
RT @TwitterEng
Learn about fusing Elasticsearch with neural networks to identify data.
We discuss how we annotated our data to a defined taxonomy by leveraging machine learning in our data platform.
https://blog.twitter.com/engineering/en_us/topics/insights/2021/fusing-elasticsearch-with-neural-networks-to-identify-data.html
ICML 2021 is going to show us a new self-supervised embedding algorithm called Barlow Twins. It's name is ofc a reference to the siamese networks, but the key novelty is lack of contrastive samples.
Preprint by Zbontar et al.:
https://arxiv.org/abs/2103.03230
There is a similar, less known method called Invariant Information Clustering which also uses augmented images as a positive anchor
RT @josh_tobin_
In the real world, data distributions are almost never static.
So if you want to use ML in production, your goal should be to build a continual learning machine, not a static model.
Our article with @SzymonSzott about deep reinforcement learning for contention window optimization is available in the IEEE Xplore repository.
