Open Sourcing the Hunt for Exoplanets

March 8, 2018

Table of Contents

Recently, we discovered two exoplanets by training a neural network to analyze data from NASA’s Kepler space telescope and accurately identify the most promising planet signals. And while this was only an initial analysis of ~700 stars, we consider this a successful proof-of-concept for using machine learning to discover exoplanets, and more generally another example of using machine learning to make meaningful gains in a variety of scientific disciplines (e.g. healthcare, quantum chemistry, and fusion research)

Source: googleblog.com

Reptile: A Scalable Meta-Learning Algorithm

We’ve developed a simple meta-learning algorithm called Reptile which works by repeatedly sampling a task, performing stochastic gradient descent on it, and updating the initial parameters towards the final parameters learned on that task. This method performs as well as MAML, a broadly applicable meta-learning algorithm, while being simpler to implement and more computationally efficient.

Bonsai AI: Using Simulink for Deep Reinforcement Learning

Simulink provides a great training environment for DRL as it allows 3rd parties like Bonsai to integrate and control simulation models from the outside. This ability is one of the basic requirements for simulation platforms to be feasible for Deep Reinforcement Learning using Bonsai AI. More requirements can be found here.

Open Sourcing the Hunt for Exoplanets

Tags :

Share :

Related Posts

Reptile: A Scalable Meta-Learning Algorithm

Bonsai AI: Using Simulink for Deep Reinforcement Learning

Your Data Is Crucial to a Robotic Age. Shouldn’t You Be Paid for It?