Natural Language Semantics With Pictures: Some Language & Vision Datasets and Potential U... https://arxiv.org/abs/1904.07318

A Realistic Dataset and Baseline Temporal Model for Early Drowsiness Detection. https://arxiv.org/abs/1904.07312

Brain Tumor Segmentation on MRI with Missing Modalities. https://arxiv.org/abs/1904.07290

entureBeat article on the singing voice conversion system developed at FAIR-Tel Aviv.
This can transform someone's singing voice into someone else's voice

https://venturebeat.com/2019/04/16/facebooks-ai-can-convert-one-singers-voice-into-another/

Ian Goodfellow: Generative Adversarial Networks (GANs)
@ArtificialIntelligenceArticles
This conversation with Ian led me to rethink the way I see several basic ideas in deep learning, including generative models, adversarial learning, and reasoning. I definitely enjoyed it and hope you do as well.



https://www.youtube.com/watch?v=Z6rxFNMGdn0&fbclid=IwAR1P-iKyeed3-8GziQp59ZxPBkHZ9yX1fK1slAUK1MIbQJPK7cy0exDzTQ8

https://t.iss.one/ArtificialIntelligenceArticles

Standards for AI Governance: International Standards to Enable Global Coordination in AI Research & Development
By Peter Cihon,Research Affiliate, Center for the Governance of AI Future of Humanity Institute, University of Oxford: https://www.fhi.ox.ac.uk/wp-content/uploads/Standards_-FHI-Technical-Report.pdf
#ArtificialIntelligence

Datasheets for Datasets

https://arxiv.org/abs/1803.09010
#Databases #ArtificialIntelligence #AIEthics #Ethics #MachineLearning

80 Best Data Science Books That Are Worthy Reading
https://www.octoparse.com/blog/80-best-data-science-books-that-are-worthy-reading

Advanced Machine Learning: Neural Architecture Search

A great lecture by Debadeepta Dey from Microsoft Research:
https://www.youtube.com/watch?v=wL-p5cjDG64

Neural Painters: A learned differentiable constraint for generating brushstroke paintings
By Reiichiro Nakano https://arxiv.org/abs/1904.08410
GitHub: https://github.com/reiinakano/neural-painters/tree/master/notebooks
Colab notebooks: https://colab.research.google.com/github/reiinakano/neural-painters/

4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks
Choy et al.: https://arxiv.org/abs/1904.08755
#ArtificialIntelligence #DeepLearning #NeuralNetworks

Single Cortical Neurons as Deep Artificial Neural Networks
Beniaguev et al.: https://www.biorxiv.org/content/biorxiv/early/2019/04/18/613141.full.pdf
#DeepLearning #MachineLearning #SynapticIntegration

A List Of Top 10 Deep Learning Papers, The 2018 Edition https://www.techleer.com/articles/517-a-list-of-top-10-deep-learning-papers-the-2018-edition/

Deep Learning State of the Art (2019) by lex fridman
https://youtu.be/53YvP6gdD7U @ArtificialIntelligenceArticles

Harnessing Organizational Knowledge for Machine Learning
https://ai.googleblog.com/2019/03/harnessing-organizational-knowledge-for.htm

Style Transfer Tutorial
Colab: https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/r2/tutorials/generative/style_transfer.ipynb
#ArtificialIntelligence #DeepLearning #MachineLearning

These are seven ways Google’s image analysis tools classifies uploaded images:
Faces
Objects
Labels
Web Entities
Text
Properties
Safe Search

https://www.searchenginejournal.com/google-cloud-vision-tool/304237/

In medical imaging, computer aided detection (CAD) systems are available for detection of tumors and diagnosis of diseases from images acquired by imaging modalities like CT, MRI, X-ray and ultrasound. AI techniques used in computer vision such as object detection and segmentation offer unique possibilities to help radiologists make diagnosis faster and more accurately, leading to prioritization of cases, better patient care, and reduction in cost.

DNN Architecture for High Performance Prediction on Natural Videos Loses Submodule's Abil... https://arxiv.org/abs/1904.07969

A Comprehensive Study of Alzheimer's Disease Classification Using Convolutional Neural Ne... https://arxiv.org/abs/1904.07950

IAN: Combining Generative Adversarial Networks for Imaginative Face Generation. https://arxiv.org/abs/1904.07916