Variational Prototyping-Encoder: One-Shot Learning with Prototypical Images. https://arxiv.org/abs/1904.08482

A large-scale field test on word-image classification in large historical document collec... https://arxiv.org/abs/1904.08421

Neural Painters: A learned differentiable constraint for generating brushstroke paintings. https://arxiv.org/abs/1904.08410

A Selective Overview of Deep Learning
Fan et al.: https://arxiv.org/abs/1904.05526
#ArtificialIntelligence #DeepLearning #MachineLearning

A Neural Parametric Singing Synthesizer

https://www.dtic.upf.edu/~mblaauw/NPSS/

MR-based CT Generation (DCCC) Tensorflow Project

This repository is the implementations of the paper "MR-based Synthetic CT Generation using Deep Convolutional Neural Network Method,"
https://github.com/ChengBinJin/MRI-to-CT-DCNN-TensorFlow

Fooling automated surveillance cameras: adversarial patches to attack person detection"
Thys et al.: https://arxiv.org/abs/1904.08653
#ArtificialIntelligence #DeepLearning #MachineLearning

CS294-158 Deep Unsupervised Learning SP19
https://www.youtube.com/channel/UCf4SX8kAZM_oGcZjMREsU9w/videos?fbclid=IwAR01HPq5VLKX8aXwFbB4UWs5mZVgOOYOLUpjx_O2015rso-7U_H32hu-AKE

Self-Attention Generative Adversarial Networks
Zhang et al.: https://arxiv.org/abs/1805.08318
#DeepLearning #GenerativeAdversarialNetworks #MachineLearning

Functional brain network architecture supporting the learning of social networks in humans @ArtificialIntelligenceArticles
Tompson et al.: https://psyarxiv.com/r46gj/
#brainnetworks #neuroscience #socialnetworks #neuralnetworks @ArtificialIntelligenceArticles

Tesla’s new self-driving chip is here, and this is your best look yet
https://www.theverge.com/2019/4/22/18511594/tesla-new-self-driving-chip-is-here-and-this-is-your-best-look-yet

Using genetic algorithm for reproducing images

https://github.com/ahmedfgad/GARI

Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features
Zhang et al.: https://arxiv.org/abs/1904.10014
#ArtificialIntelligence #DeepLearning #MachineLearning

"The anticipating brain is not a scientist: the free-energy principle from an ecological-enactive perspective"
Bruineberg et al.: https://www.ncbi.nlm.nih.gov/pubmed/30996493?dopt=Abstract
#ActiveInference #FreeEnergyPrinciple #Metastability

NVIDIA Tesla T4 GPUs are now available in Colab
Faster computations with more available memory.
Read more: https://cloud.google.com/blog/products/ai-machine-learning/nvidia-tesla-t4-gpus-now-available-in-beta
#ArtificialIntelligence #Colab #DeepLearning #MachineLearning

Introducing SuperGLUE: A New Hope Against Muppetkind
Blog by Alex Wang: https://medium.com/@wang.alex.c/introducing-superglue-a-new-hope-against-muppetkind-2779fd9dcdd5
#MachineLearning #NLP #BigData

Machine learning and complex biological data

By Chunming Xu and Scott A. Jackson

Machine learning has demonstrated potential in analyzing large, complex biological data. In practice, however, biological information is required in addition to machine learning for successful application.
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1689-0
#artificialintelligence #machinelearning #deeplearning #biology #genomics

Toshiba's breakthrough algorithm realizes world's fastest, largest-scale combinatorial optimization

Toshiba Corporation has realized a major breakthrough in combinatorial optimization—the selection of the best solutions from among an enormous number of combinatorial patterns—with the development of an algorithm that delivers the world's fastest and largest-scale performance, and an approximately 10-fold improvement over current methods. Toshiba's new method can be applied to such daunting but essential tasks as identifying efficient delivery routes, determining the most effective molecular structures to investigate in new drug development, and building portfolios of profitable financial products.
https://m.phys.org/news/2019-04-toshiba-breakthrough-algorithm-world-fastest.html

#optimization #algorithms #computing