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ArtificialIntelligenceArticles
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EfficientDet: Scalable and Efficient Object Detection
New scaling techniques:
Backbone: we employ the more advanced EfficientNets as our backbone networks.
BiFPN: we propose a new bi-directional feature network, named BiFPN, to enable easy and fast feature fusion. In addition to the bi-directional topology, we also propose a new fast normalized fusion that enables better fusion with negligible latency cost.
Scaling: we propose to use a single compound scaling factor to govern the network depth, width, and resolution for all backbone, feature network, and prediction networks.
Paper: https://arxiv.org/abs/1911.09070
Code: https://github.com/google/automl/tree/master/efficientdet
COVID-19 Chatbot
"Training a chatbot who will be able to answer questions from the public about COVID-19"
Just ask any questions you have:
https://covid19.dialogue.co/?lng=en#/info
@ArtificialIntelligenceArticles
Thousands of questions are needed to train this chatbot well :)
H / T Sasha Lu
#Chatbot #covid19 #DeepLearning
Forwarded from Lex Fridman
I look for the good in people. Sometimes I get hurt for it, but it's rare and it's worth it. You may hear me say optimistic things that sound naive. I'm not naive. I've read too much history to be naive. I just think love wins out over the darker parts of human nature in the end.
@lexfridman
Regarding the continuation of my Supersymmetric artificial neural network model that I began in ~2016, here are some discussions of mine on Physics forums in 2017, as well as other resources:

1. Forum question of mine that was discussed: "Is it possible to create a ‘Transverse Field Ising Spin’-compatible Super Hamiltonian?"

https://www.physicsoverflow.org/39603/possible-create-transverse-ising-compatible-hamiltonian

2. Forum question of mine that was discussed: "Can One Compose A ‘Transverse Field Ising Spin’-Compatible Super Hamiltonian?"

http://www.scienceforums.com/topic/30421-can-one-compose-a-%E2%80%98transverse-field-ising-spin%E2%80%99-compatible-super-hamiltonian/

3. The Supersymmetric artificial neural network model:

https://github.com/JordanMicahBennett/Supersymmetric-artificial-neural-network

4. Mitchell Porter's view on the Supersymmetric Artificial Neural Network etc (Section 5 "USE OF SUPERMATH IN MACHINE LEARNING?"):

https://www.researchgate.net/publication/332103958_2019_Applications_of_super-mathematics_to_machine_learning
Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control
Christian Schroeder de Witt et al.: https://arxiv.org/abs/2003.06709
#MachineLearning #ArtificialIntelligence #ReinforcementLearning
Representation Learning Through Latent Canonicalizations
Litany et al.: https://arxiv.org/abs/2002.11829
#ArtificialIntelligence #DeepLearning #RepresentationLearning
Unboxing the "Black Box": Learning Interpretable Deep Learning Features of Brain Aging
https://prism.ucalgary.ca/handle/1880/111255
Corona Virus Media Watch launched by UNESCO’s International Research Centre on Artificial Intelligence in Slovenia
http://videolectures.net/site/news/corona_live/
https://news.1rj.ru/str/ArtificialIntelligenceArticles
Help us scale #COVID19 detection over the phone.

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We hope this data leads to techniques to help diagnosis of #COVID19 over the phone.