Long Short-Term Memory: From Zero to Hero with PyTorch
https://blog.floydhub.com/long-short-term-memory-from-zero-to-hero-with-pytorch/

Efficient Exploration via State Marginal Matching

Lee et al.

Blog: https://sites.google.com/view/state-marginal-matching
Paper: https://arxiv.org/abs/1906.05274
Code: https://github.com/RLAgent/state-marginal-matching

#ArtificialIntelligence #MachineLearning #ReinforcementLearning

Learning the Depths of Moving People by Watching Frozen People

Li et al.: https://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Learning_the_Depths_of_Moving_People_by_Watching_Frozen_People_CVPR_2019_paper.pdf

#ArtificialIntelligence #DeepLearning #MachineLearning

Theoretical Physics for Deep Learning workshop at #ICML2019

Slides and videos: https://sites.google.com/view/icml2019phys4dl/schedule?authuser=0

#Physics #DeepLearning

Adaptive Neural Trees
Tanno et al.: https://arxiv.org/abs/1807.06699
#ArtificialIntelligence #ComputerVision #EvolutionaryComputing #MachineLearning #PatternRecognition

ProMP: Proximal Meta-Policy Search

Builds on MAML, E-MAML and DiCE.

Rothfuss et al.: https://arxiv.org/pdf/1810.06784.pdf

Code: https://github.com/jonasrothfuss/promp

#ArtificialIntelligence #DeepLearning #MachineLearning #MetaLearning #ReinforcementLearning

From CVPR 2019: Turning Doodles into Stunning, Photorealistic Landscapes.

NVIDIA research harnesses generative adversarial networks to create highly realistic scenes.

Artists can use paintbrush and paint bucket tools to design their own landscapes with labels like river, rock and cloud

https://www.profillic.com/paper/arxiv:1903.07291

From CVPR 2019: A team of scientists at Microsoft and academic collaborators reconstruct color images of a scene from the point cloud.

https://www.profillic.com/paper/arxiv:1904.03303

Event2Mind: Commonsense Inference on Events, Intents, and Reactions ACL 2018

demo https://uwnlp.github.io/event2mind/

paper https://homes.cs.washington.edu/~msap/pdfs/rashkin2018event2mind.pdf

Comprehensive Introduction to Neural Network Architecture
https://towardsdatascience.com/comprehensive-introduction-to-neural-network-architecture-c08c6d8e5d98

SLIDES
What can Statistical Machine Translation teach Neural Text Generation about Optimization

Graham Neubig

@ NAACL Workshop on Methods for Optimizing and Evaluating Neural Language Generation6/6/2019
https://www.phontron.com/slides/neubig19neuralgen.pdf

Course 3 of the deeplearning.ai TensorFlow Specialization is now available on Coursera! You’ll learn how to build natural language processing systems using TensorFlow. Enroll in the Specialization for $49/month or audit for free: https://www.coursera.org/specializations/tensorflow-in-practice

Losing Confidence in Quality: Unspoken Evolution of Computer Vision Services," Cummaudo et al.: https://arxiv.org/abs/1906.07328

A new research paper by Geoffry E.Hinton , Adam R. Kosiorek, Sara Sabour, Yee Whye Teh

a new version of capsule networks, called Stacked Capsule Autoencoders


https://arxiv.org/abs/1906.06818

https://t.iss.one/ArtificialIntelligenceArticles

Stanford Machine Learning Class Notes (CS229)
BY TANUJIT CHAKRABORTY

Download Link: https://www.ctanujit.org/uploads/2/5/3/9/25393293/machine_learning_notes__cs229_.pdf

Published in CVPR 2019: Stanford researchers address weakly-supervised action alignment and segmentation in videos, where only the order of occurring actions is available during training.

Their model outperforms the current state-of-the-art

https://www.profillic.com/paper/arxiv:1901.02598

Open source PyRobot, a lightweight, high-level interface that lets AI researchers get up and running with robotics experiments in just hours. No specialized robotics expertise needed
https://ai.facebook.com/blog/open-sourcing-pyrobot-to-accelerate-ai-robotics-research/

https://www.google.com/amp/s/amp.cnn.com/cnn/2019/04/14/africa/google-ai-center-accra-intl/index.html Google Google AI

Kaggle dataset usability ratings on 17000+ public datasets

Here: https://www.kaggle.com/datasets

#ArtificialIntelligence #DeepLearning #MachineLearning