Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
Alexia Jolicoeur-Martineau and Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
#GenerativeAdversarialNetworks #RelativisticGAN #SVM

Evaluating the Factual Consistency of Abstractive Text Summarization
Kryscinski et al.: https://arxiv.org/abs/1910.12840
#ArtificialIntelligence #DeepLearning #NaturalLanguageProcessing

From ICCV 2019: Great applications for the 3D scanning industry!

https://www.profillic.com/paper/arxiv:1909.00883

FACSIMILE: Fast and Accurate Scans From an Image in Less Than a Second

Yoshua Bengio on Human vs Machine Intelligence
https://medium.com/syncedreview/yoshua-bengio-on-human-vs-machine-intelligence-5f55ec8de9cf https://t.iss.one/ArtificialIntelligenceArticles

Grandmaster level in StarCraft II using multi-agent reinforcement learning

#AI #artificialintelligence
#DeepLearning #ReinforcementLearning
#deepmind

Blog: https://deepmind.com/blog/article/AlphaStar-Grandmaster-level-in-StarCraft-II-using-multi-agent-reinforcement-learning

https://www.nature.com/articles/s41586-019-1724-z

GQN — Generative Query Network
The agent infers the image from a viewpoint based on the pre-knowledge of the environment and viewpoints
Comprised of three architectures:
The representation architecture takes images from different viewpoints to yield a concise abstract scene representation.
The generation architecture generates an image for a new query viewpoint.
The inference architecture served as the encoder in a variational autoencoder provides a way to train the other two architectures in an unsupervised manner.
Blogs
Very intuitive explanation:
https://xlnwel.github.io/blog/representation%20learning/GQN/
DeepMind: https://deepmind.com/blog/article/neural-scene-representation-and-rendering

Artificial Intelligence: Reality vs Hype

Landing AI Founder and CEO Andrew Ng sits down with Bloomberg’s Austin Carr at Sooner Than You Think in Brooklyn. (Source: Bloomberg)

https://www.bloomberg.com/news/videos/2019-10-30/artificial-intelligence-reality-vs-hype-video

BPE-Dropout: Simple and Effective Subword Regularization
https://arxiv.org/abs/1910.13267

A New Spin on the Quantum Brain
https://www.quantamagazine.org/a-new-spin-on-the-quantum-brain-20161102/

Keras / TPU integration in Tensorflow 2.1 (unreleased)
Google Cloud Platform : https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/fast-and-lean-data-science/README-TF2.1.md
#Keras #TPU #Tensorflow https://t.iss.one/ArtificialIntelligenceArticles

Learning Data Manipulation for Augmentation and Weighting

Zhiting Hu, Bowen Tan, Ruslan Salakhutdinov, Tom Mitchell, Eric P. Xing : https://arxiv.org/abs/1910.12795

#ArtificialIntelligence #MachineLearning #ReinforcementLearning

Neural Density Estimation and Likelihood-free Inference
George Papamakarios : https://arxiv.org/pdf/1910.13233.pdf
#Bayesian #NeuralDensityEstimation #Inference

Introduction to Capsules by Sara Sabour, Google


https://www.youtube.com/watch?v=zRg3IuxaJ6I

https://t.iss.one/ArtificialIntelligenceArticles

Improving Generalization in Meta Reinforcement Learning using Learned Objectives
Louis Kirsch, Sjoerd van Steenkiste, Jürgen Schmidhuber : https://arxiv.org/abs/1910.04098
#ArtificialIntelligence #MetaReinforcementLearning #ReinforcementLearning

Engineers at Google have unveiled Dex, a prototype functional language designed for array processing. Array processing is a cornerstone of the math used in machine learning applications and other computationally intensive work.

#dex #machinelearning #linearalgebra

https://openreview.net/pdf?id=rJxd7vsWPS