SlowFast Networks for Video Recognition

Feichtenhofer et al.: https://arxiv.org/abs/1812.03982

#MachineLearning #ComputerVision #DeepLearning #PatternRecognition #Technology

The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning

By Yang-Hui He: https://arxiv.org/abs/1812.02893

#HighEnergyPhysics #MathematicalPhysics #AlgebraicGeometry #MachineLearning #ArtificialIntelligence @ArtificialIntelligenceArticles

Weighted Risk Minimization & Deep Learning. https://arxiv.org/abs/1812.03372

No Peek: A Survey of private distributed deep learning. https://arxiv.org/abs/1812.03288

Top 100 most discussed academic papers" (across all fields) this year
https://www.altmetric.com/top100/2018/ @ArtificialIntelligenceArticles

AI book to lead company by andrew ng
https://d6hi0znd7umn4.cloudfront.net/content/uploads/2018/12/AI-Transformation-Playbook.pdf @ArtificialIntelligenceArticles

TensorSpace is a neural network 3D visualization framework

Built on TensorFlow.js, Three.js and Tween.js: https://tensorspace.org/

#NeuralNetworks #ArtificialInteligence #DeepLearning #MachineLearning #Visualization #Technology

Style-Based Generator Architecture for Generative Adversarial Networks" video shows generating realistic looking faces, cats, cars, rooms, etc. from a GAN.

The video is great: https://www.youtube.com/watch?&v=kSLJriaOumA

Paper: https://arxiv.org/abs/1812.04948

Top 20 Artificial Intelligence Articles in 2018 https://www.marktechpost.com/2018/12/29/top-20-artificial-intelligence-articles-in-2018/ @ArtificialIntelligenceArticles

Learn Machine Learning from Top 50 Articles for the Past Year (v.2019)
https://medium.mybridge.co/learn-machine-learning-from-top-50-articles-for-the-past-year-v-2019-15842d0b82f6

Training Deep Capsule Networks

by David Peer, Sebastian Stabinger, Antonio Rodriguez-Sanchez

https://arxiv.org/abs/1812.09707 @ArtificialIntelligenceArticles

Deep Learning on Graphs: A Survey

Nice review of graph nets

https://arxiv.org/abs/1812.04202

#ArtificialIntelligence #MachineLearning #DataScience

The statistical mechanics of Twitter

Paper : https://arxiv.org/abs/1812.07029

#Physics #Society #StatisticPhysics

paper from Andrew Y. Ng : Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network


Awni Y. Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H. Tison, Codie Bourn, Mintu P. Turakhia & Andrew Y. Ng

https://www.nature.com/articles/s41591-018-0268-3

https://t.iss.one/ArtificialIntelligenceArticles

A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs

Paper by Lindsey et al.: https://arxiv.org/abs/1901.00945

#Neurons #Cognition #MachineLearning #EvolutionaryComputing

Neural Ordinary Differential Equations

https://blog.acolyer.org/2019/01/09/neural-ordinary-differential-equations/

#artificialintelligence #deeplearning #machinelearning

NeurIPS 2018

Videos: https://nips.cc/Conferences/2018/Videos

#artificialintelligence #deeplearning #machinelearning #NeurIPS #NeurIPS2018

First lecture on Deep Learning Basics is up on YouTube (see link). It's an introductory lecture overviewing the basics of deep learning.

https://www.youtube.com/watch?v=O5xeyoRL95U

Slides for this lecture:

https://www.dropbox.com/s/c0g3sc1shi63x3q/deep_learning_basics.pdf

Website: https://deeplearning.mit.edu/
GitHub repo with tutorials: https://github.com/lexfridman/mit-deep-learning

For those around MIT, the course is open to all. It runs every day in January at 3pm

https://towardsdatascience.com/the-abcs-of-machine-learning-experts-who-are-driving-the-world-in-ai-2995a8115bea

Graph Wavelet Neural Network (ICLR 2019) -- Pytorch implementation
Paper: https://openreview.net/forum?id=H1ewdiR5tQ

PyTorch: https://github.com/benedekrozemberczki/GraphWaveletNeuralNetwork

News Feature: What are the limits of deep learning?

https://www.pnas.org/content/116/4/1074

#artificialintelligence #deeplearning #machinelearning