Coronavirus Datasets from Every Country with Confirmed Cases
@ArtificialIntelligenceArticles

https://lionbridge.ai/datasets/coronavirus-datasets-from-every-country/

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?"

https://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

CS472 Data science and AI for COVID-19
Zou et al.: https://sites.google.com/view/data-science-covid-19/
#ArtificialIntelligence #Covid19 #DataScience https://t.iss.one/ArtificialIntelligenceArticles

Lucid
A collection of infrastructure and tools for research in neural network interpretability : https://github.com/tensorflow/lucid
#Tensorflow #Interpretability #Visualization #MachineLearning #Colab

New open source dataset for abstractive summarisation

https://medium.com/curation-corporation/teaching-an-ai-to-abstract-a-new-dataset-for-abstractive-auto-summarisation-5227f546caa8

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
https://videolectures.net/site/news/corona_live/
https://t.iss.one/ArtificialIntelligenceArticles

Help us scale #COVID19 detection over the phone.

If you have a #COVID19 diagnosis or are healthy, consider recording a breathing sample anonymously at
https://www.breatheforscience.com/


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

Gradient Boosting Neural Networks: GrowNet
Badirli et al.: https://arxiv.org/abs/2002.07971v1
#ArtificialIntelligence #GradientBoosting #NeuralNetworks

Backpropagation, Intuitions (Stanford CS231n)
Link: https://cs231n.github.io/optimization-2/

Generalizable Autonomy for Robot Manipulation https://www.youtube.com/watch?v=8Kn4Gi8iSYQ

Understanding the limits of convolutional neural networks — one of AI’s greatest achievements

https://thenextweb.com/neural/2020/03/20/understanding-the-limits-of-convolutional-neural-networks-one-of-ais-greatest-achievements-syndication/

MIT offers free video lectures and online materials from more than 2,400 courses, including intro classes in computer science, AI and algorithms.
Browse our open CS courses here:

https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/