ICML Highlight: Contrastive Divergence for Combining Variational Inference and MCMC

https://www.inference.vc/icml-highlight-contrastive-divergence-for-variational-inference-and-mcmc/?fbclid=IwAR1iZXz9zvewdImeZ9lw8BS2a9gk4U7enTYf6x9_pYnZpAhOBO7GILWGiBM

ICML 2019 top papers and highlights https://amicki.co/2019/06/14/ai-weekly-icml-2019-top-papers-and-highlights/

Get SMPL-X, an expressive 3D body that extends the popular SMPL body model with an expressive face and articulated hands. Use SMPLify-X to estimate SMPL-X from a single image. This appears at CVPR.
Project: https://smpl-x.is.tue.mpg.de/
Video: https://www.youtube.com/watch?v=XyXIEmapWkw&feature=youtu.be
Code: https://lnkd.in/dvPDjkF

Semantic Image Synthesis with Spatially-Adaptive Normalization
paper : https://arxiv.org/abs/1903.07291

* code : https://github.com/taki0112/SPADE-Tensorflow

Integrate logic and deep learning with #SATNet, a differentiable SAT solver! #icml2019

Paper: https://arxiv.org/abs/1905.12149
Code: https://github.com/locuslab/SATNet

NIPS 2017 Invited talk "Deep Reinforcement Learning with Subgoals"
By David Silver: https://vimeo.com/249557775
#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks #ReinforcementLearning

Deep Learning: AlphaGo Zero Explained In One Picture
By L.V.: https://api.ning.com/files/G3detyndwpXvT8Py3CFA1rtuPS549-KcvNCPjfyaORlWtrBVjnT7MSsnV5zQmlOYZg8n9cIqQqf2u4GMq0VHnN1AE-nlYFnx/porc.png
#AlphaGo #ArtificialIntelligence #DeepLearning #NeuralNetworks #ReinforcementLearning

Deep RL Bootcamp

By Pieter Abbeel, Rocky Duan, Peter Chen, Andrej Karpathy et al.: https://sites.google.com/view/deep-rl-bootcamp/lectures

#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks #ReinforcementLearning

This is a PyTorch implementation of "Capsule Graph Neural Network" (ICLR 2019) that I made. On most standard benchmark datasets it is considered to be the state-of-the-art deep learning model for graph classification. It can be used for molecular graph classification, fraud detection and so on. Enjoy!

https://github.com/benedekrozemberczki/CapsGNN

Quantum curious ?


https://www.dwavesys.com/take-leap

These must-read ML & data science books are completely free:

https://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html


#weekendmotivation

PyTorch Developer Conference 2018
https://www.facebook.com/pytorch/videos/169366590639145/

A Game of Words: Vectorization, Tagging, and Sentiment Analysis
https://towardsdatascience.com/a-game-of-words-vectorization-tagging-and-sentiment-analysis-c78ff9a07e42

Best Papers Reinforcement Learning for Real Life
ICML 2019 Workshop

1. Lyapunov-based Safe Policy Optimization for Continuous Control

https://openreview.net/forum?id=SJgUYBVLsN

2. challenges of Real-World Reinforcement Learning
https://openreview.net/forum?id=S1xtR52NjN


3.Horizon: Facebook’s Open Source Applied Reinforcement Learning Platform


https://openreview.net/forum?id=SylQKinLi4


4. Park: An Open Platform for Learning Augmented Computer Systems

https://openreview.net/forum?id=BkgfRbEPsE


https://t.iss.one/ArtificialIntelligenceArticles

Best paper award at #CVPR2018 main idea: study twenty five different visual tasks to understand how & when transfer learning works from one task to another, reducing demand for labelled data.

Paper: arxiv.org/pdf/1804.08328
Data: taskonomy.stanford.edu https://t.iss.one/ArtificialIntelligenceArticles

#CVPR2019 Videos :Videos from #CVPR2019 talks are gradually uploaded on YouTube .if you missed #CVPR2019 here is the treasure trove of videos thanks to the ComputerVisionFoundation


https://www.youtube.com/channel/UC0n76gicaarsN_Y9YShWwhw/videos

https://t.iss.one/ArtificialIntelligenceArticles

Here is a wonderful Self Supervised Learning 122 page lecture notebook by Andrew Zisserman from Deepmind
Download Link: https://project.inria.fr/paiss/files/2018/07/zisserman-self-supervised.pdf

Cool paper written by Yoshua Bengio’s MILA team.

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

Paper: arxiv.org/abs/1905.10437
GitHub for M4 dataset: https://github.com/M4Competition/M4-methods/tree/master/Dataset
#timeseries #deeplearning

Divide and Conquer the Embedding Space for Metric Learning, CVPR 2019

code https://github.com/CompVis/metric-learning-divide-and-conquer

Beyond data and model parallelism for deep neural networks
https://blog.acolyer.org/2019/06/12/beyond-data-and-model-parallelism/

Research suggests that fatherhood could increase oxytocin (a hormone associated with social bonding) and decrease testosterone (a hormone associated with aggression) as it develops neurons and alters activity in the brain.

https://m.washingtontimes.com/news/2019/jun/15/fatherhood-changes-mens-brains-and-minds-studies-s/