Disentangling Disentanglement in Variational Autoencoders

Mathieu et al.: https://proceedings.mlr.press/v97/mathieu19a.html

#DeepLearning #VariationalAutoencoders #VAE

MelNet: A Generative Model for Audio in the Frequency Domain

Sean Vasquez, Mike Lewis: https://arxiv.org/abs/1906.01083

Blog: https://sjvasquez.github.io/blog/melnet/

#ArtificialIntelligence #AudioProcessing #MachineLearning

Welcome this Chinese DeepLearning Chip called “Tianjic”.

Paper: https://www.nature.com/articles/s41586-019-1424-8

It can run traditional deep learning code and also perform "neuromorophic" operations in the same circuitry.

Ten trends in Deep learning NLP
https://blog.floydhub.com/ten-trends-in-deep-learning-nlp/

"Deep Boltzmann Machines"
Ruslan Salakhutdinov and Geoffrey Hinton : https://proceedings.mlr.press/v5/salakhutdinov09a/salakhutdinov09a.pdf
#BoltzmannMachines #DeepBoltzmannMachines #DeepLearning

CS231N: Convolutional Neural Networks for Visual Recognition

Stanford University School of Engineering : https://www.youtube.com/playlist?list=PLzUTmXVwsnXod6WNdg57Yc3zFx_f-RYsq

#ArtificialIntelligence #MachineLearning #NeuralNetworks

CS224N Natural Language Processing with Deep Learning

Stanford University School of Engineering
:
https://www.youtube.com/playlist?list=PLU40WL8Ol94IJzQtileLTqGZuXtGlLMP_

#NaturalLanguageProcessing #MachineLearning #DeepLearning

Twenty things I wish I’d known when I started my PhD

https://www.nature.com/articles/d41586-018-07332-x

Build a self-driving for about $300 using a RasberryPi. 😊

https://towardsdatascience.com/deeppicar-part-1-102e03c83f2c

Introduction to Reinforcement Learning
By DeepMind. YouTube: https://www.youtube.com/watch?list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ&time_continue=5&v=2pWv7GOvuf0
#deeplearning #artificialintelligence #reinforcementlearning

Tutorial on Graph Neural Networks for Computer Vision and Beyond (Part 1)
https://medium.com/@BorisAKnyazev/tutorial-on-graph-neural-networks-for-computer-vision-and-beyond-part-1-3d9fada3b80d

NeurIPS 2019 : Disentanglement Challenge
Blog by Max Planck Institute for Intelligent Systems: https://www.aicrowd.com/challenges/neurips-2019-disentanglement-challenge
#DeepLearning #Disentanglement #NeurIPS #NeurIPS2019

New Frontiers of Automated Mechanism Design for Pricing and Auctions by Maria-Florina Balcan, @mldcmu, Tuomas Sandholm, Ellen Vitercik @csdatcmu

Learn more → https://mld.ai/y1m
Tutorial Video Part I: https://youtu.be/buK3KXZcGAI
Tutorial Video Part II: https://youtu.be/T8gaK4Yw4zI

#MechanismDesign #GameTheory #Tutorial #MachineLearning #Optimization #ML

We just released a high-performance graph embedding system called GraphVite, which supports a variety of applications including node embeddings, knowledge graph embeddings and graph&high-dimensional data visualization. It is super fast, which only takes around one minute to learn node embebeddings for a graph with one million node. We benchmarked a variety of models including DeepWalk, LINE, node2vec, TransE, DistMult, ComplEx, SimplE, RotatE, LargeVis... More information is available at: https://graphvite.io/

"Automating Inference, Learning, and Design using Probabilistic Programming"
Tom Rainforth, Wolfson College, University of Oxford : https://www.robots.ox.ac.uk/~twgr/assets/pdf/rainforth2017thesis.pdf
#ProbabilisticProgramming #MonteCarloinference #MarkovChainMonteCarlo

SLIDES
Beyond Domain Randomization
Josh Tobin
https://josh-tobin.com/assets/pdf/BeyondDomainRandomization_Tobin_RSS19.pdf

Paper: Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
https://arxiv.org/pdf/1703.06907.pdf

Learning to Train with Synthetic Humans
https://arxiv.org/pdf/1908.00967.pdf

Weight Agnostic Neural Networks
https://weightagnostic.github.io/
code https://github.com/google/brain-tokyo-workshop/tree/master/WANNRelease
paper https://arxiv.org/pdf/1906.04358.pdf

Hands-on Global Model Interpretation
https://towardsdatascience.com/hands-on-global-model-interpretation-3bb4264732b5