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ThunderNet: Towards Real-time Generic Object Detection
https://arxiv.org/pdf/1903.11752v1.pdf
Jetson Nano šŸ˜

NVIDIA Jetson Nano enables the development of new small, low-power AI systems. It opens new worlds of embedded IoT applications, including home robots.

It's a small AI computer to create intelligent systems:https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-nano/

#ArtificialIntelligence #DeepLearning #MachineLearning #ReinforcementLearning #Robotics
ā€œNip Fake News in the bud: Introducing PreFakesā€ by UnFound
https://medium.com/unfound-news/nip-fake-news-in-the-bud-introducing-prefakes-69f768af2dc6
Machine learning and artificial intelligence in the quantum domain"
By Vedran Dunjko, Hans J. Briegel: https://arxiv.org/abs/1709.02779
#QuantumPhysics #ArtificialIntelligence #ComputerVision #MontrealAI #PatternRecognition
DeepMindAI: Today we're releasing a large-scale extendable dataset of mathematical questions, for training (and evaluating the abilities of) neural models that can reason algebraically.

Paper: https://arxiv.org/abs/1904.01557 Code and data : https://github.com/deepmind/mathematics_dataset
Learning Discrete Structures for Graph Neural Networks

Franceschi et al.: https://arxiv.org/abs/1903.11960

#DeepLearning #MachineLearning #NeuralNetworks
Climate change: How can AI help?
Applying machine learning to address the problems of climate change.
Call For Submissions: https://www.climatechange.ai
#artificialintelligence #climatechange #machinelearning
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HoloGAN: Unsupervised learning of 3D representations from natural images.
Another demonstration that hardcoding prior knowledge about the world is generally a good idea.
Blog: https://www.monkeyoverflow.com/#/hologan-unsupervised-learning-of-3d-representations-from-natural-images/
Paper: https://arxiv.org/abs/1904.01326
A review of INFINITE POWERS in nature: From counting with stones to artificial intelligence: the story of calculus. Anil Ananthaswamy savours a history of the mathematics used to track changes in everything from DNA to machine learning.

https://www.nature.com/articles/d41586-019-01038-4