A Selective Overview of Deep Learning https://www.princeton.edu/~congm/Publication/DL_survey/DL_survey.pdf

#weekend_read
Paper-Title: Safe Reinforcement Learning with Scene Decomposition for Navigating Complex Urban Environments
Link to the paper: https://arxiv.org/pdf/1904.11483.pdf
#Stanford #HRI
TL;DR: [1] They presented a decision-making framework for autonomously navigating urban intersections.

[2] Secondly, they introduced a learned belief updater that uses an ensemble of RNNs to estimate the location of vehicles behind obstacles and is robust to perception errors.

[3] Further they improved upon pure reinforcement learning methods by using a model checker to enforce safety guarantees.

[4] Finally, through a scene decomposition method they demonstrated how to efficiently scale the algorithm to scenarios with multiple cars and pedestrians.

Machine Learning From Scratch

Bare bones #Python implementations of #MachineLearning models and algorithms with a focus on accessibility.

By Erik Linder-Noren: https://github.com/eriklindernoren/ML-From-Scratch...
See More

Deep Learning: AlphaGo Zero Explained In One Picture

By L.V.: https://api.ning.com/files/G3detyndwpXvT8Py3CFA1rtuPS549-KcvNCPjfyaORlWtrBVjnT7MSsnV5zQmlOYZg8n9cIqQqf2u4GMq0VHnN1AE-nlYFnx/porc.png

A Gentle Introduction to 1×1 Convolutions to Reduce the Complexity of Convolutional Neural Networks
https://machinelearningmastery.com/introduction-to-1x1-convolutions-to-reduce-the-complexity-of-convolutional-neural-networks/

Ironically, Yuval Noah Harari's equation of B X C X D= HH, where B=biological knowledge, C=computer power, D=data, HH=human hacking in days after the 1st report of direct #brain activity to speech.

Fei Fei Li to YNH : "Okay, can I be specific? First of all the birth of AI is AI scientists talking to biologists, specifically neuroscientists, right. The birth of AI is very much inspired by what the brain does. Fast forward to 60 years later, today's AI is making great improvements in healthcare. There's a lot of data from our physiology and pathology being collected and using machine learning to help us. But I feel like you're talking about something else."

https://www.wired.com/story/will-artificial-intelligence-enhance-hack-humanity/

Mathematics for Artificial Intelligence
https://rubikscode.net/2019/04/29/mathematics-for-artificial-intelligence-linear-algebra/

Meta-Sim: Learning to Generate Synthetic Datasets
Kar et al.: https://arxiv.org/abs/1904.11621 @ArtificialIntelligenceArticles
#ComputerVision #PatternRecognition #ArtificialIntelligence

Conversation between Lex Fridman and Oriol Vinyals about DeepMind AlphaStar, StarCraft, and Language
Artificial Intelligence podcast: https://youtu.be/Kedt2or9xlo
#AlphaStar #ArtificialIntelligence #DeepLearning #ReinforcementLearning

Invertible Residual Networks
Behrmann et al.: https://arxiv.org/abs/1811.00995
#MachineLearning #ArtificialIntelligence #ComputerVision

A first in medical robotics: Autonomous navigation inside the body

https://techxplore.com/news/2019-04-medical-robotics-autonomous-body.html?fbclid=IwAR3ragCWgmUkIpQffYdtebY6mnRO7U6d__4uK_nsZKuaoTtv7JHBY0ZNUNo

Taming Recurrent Neural Networks for Better Summarization
https://www.abigailsee.com/2017/04/16/taming-rnns-for-better-summarization.html

Unsupervised Data Augmentation
Xie et al.: https://arxiv.org/abs/1904.12848
#DeepLearning #MachineLearning #ArtificialIntelligence #UnsupervisedLearning

This is lecture 3 in the series on Wasserstein GAN.. In this lecture, basic understanding of Wasserstein Generative Adversarial Network (WGAN) is discussed
https://youtu.be/aenOaiQPSYA
You can subscribe channel for more such videos
https://www.youtube.com/user/kumarahlad/featured?sub_confirmation=1

Claude Shannon, John McCarthy, Ed Fredkin and Joseph Weizenbaum
@ArtificialIntelligenceArticles
#AI in the 60's

Cornell University - Machine Learning for Intelligent Systems (CS4780/ CS5780)

I highly recommend the Cornell University's "Machine Learning for Intelligent Systems (CS4780/ CS5780)" course taught by Associate Professor Kilian Q. Weinberger.


Youtube Video Lectures:
https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
Course Lecture Notes:
https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/

#artificialintelligence #machinelearning #deeplearning #AI #algorithms #computerscience #datascience

The once-hot robotics startup Anki is shutting down after raising more than $200 million
It’s a hard, hard fall.
By Theodore Schleifer: https://www.vox.com/2019/4/29/18522966/anki-robot-cozmo-staff-layoffs-robotics-toys-boris-sofman
#ArtificialIntelligence #MachineLearning #Robotics

Yining Shi just made three Doodle Classifier experiments with Tensorflow.js:
1. Train a doodle classifier with tf.js
2. Train a doodle classifier with 345 classes
3. KNN doodle classifier
Code and demo: https://github.com/yining1023/doodleNet
#MachineLearning #TensorFlow #tensorflowjs #doodles

Activation Atlases: a new technique for visualizing what interactions between neurons can represent

By Google and OpenAI.

Blog: https://blog.openai.com/introducing-activation-atlases/
Paper: https://distill.pub/2019/activation-atlas
Code: https://github.com/tensorflow/lucid/…
Demo: https://distill.pub/2019/activation-atlas/app.html

#artificialintelligence #deeplearning #machinelearning #neuralnetworks