The evolution of art through the lens of deep convolutional networks

“The Shape of Art History in the Eyes of the Machine”, Elgammal et al.: https://arxiv.org/pdf/1801.07729.pdf

#art #artificialintelligence #deeplearning

5 NEW GENERATIVE ADVERSARIAL NETWORK (GAN) ARCHITECTURES FOR IMAGE SYNTHESIS
https://www.topbots.com/ai-research-generative-adversarial-network-images/

ThunderNet: Towards Real-time Generic Object Detection
https://arxiv.org/pdf/1903.11752v1.pdf

Interested in #ReinforcementLearning #ComputerScience https://arxiv.org/pdf/1903.11907v1.pdf

Jetson Nano 😍

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

Attention in RNNs: Understanding the mechanism with a detailed example #DeepLearning

https://medium.com/datadriveninvestor/attention-in-rnns-321fbcd64f05

“Nip Fake News in the bud: Introducing PreFakes” by UnFound
https://medium.com/unfound-news/nip-fake-news-in-the-bud-introducing-prefakes-69f768af2dc6

Reversible Adversarial Examples
Liu et al.: https://arxiv.org/abs/1811.00189
#ComputerVision #PatternRecognition #MachineLearning #MontréalAI #NeuralNetworks

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

Decentralized AI: The Power and the Necessity

https://www.youtube.com/watch?v=nt_ECiplQns

StrokeNet: A Neural Painting Environment

Zheng et al.: https://openreview.net/forum?id=HJxwDiActX

#imagegeneration #differentiablemodel #reinforcementlearning #deeplearning #modelbased

Image search using multilingual texts: a cross-modal learning approach between image and text Maxime Portaz Qwant Research

https://arxiv.org/abs/1903.11299

New awesome article from Distill.Pub
https://distill.pub/2019/visual-exploration-gaussian-processes/

Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening
https://medium.com/@jasonphang/deep-neural-networks-improve-radiologists-performance-in-breast-cancer-screening-565eb2bd3c9f ArXiV: https://arxiv.org/abs/1903.08297
GitHub (code & models): https://github.com/nyukat/breast_cancer_classifier

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

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