Weekly Review: 11/04/2017

A busy week. I finished my Aerial Robotics course! The next in the Specialization is Computational Motion Planning, which I am more excited about – mainly because the curriculum goes more towards my areas of expertise. Aerial Robotics was challenging primarily because I was doing a lot of physics/calculus which I had not attempted since a long time.

Onto the articles for this week:

Colab is now public!

Google made Colaboratory, a previously-internal tool public. ‘Colab’ is a document-collaboration tool, with the added benefits of being able to run script-sized pieces of code. This is especially useful if you want to prototype small proofs-of-concept, which can then be shared with documentation and demo-able output. I had previously used it within Google to tinker with TensorFlow, and write small scripts for database queries.

Visual Guide to Evolution Strategies

The above link is a great introduction to Evolutionary Strategies such as GAs and CMA-ES. They show a visual representation of how each of these algorithms converges on the optima from the first iteration to the last on simple problems. Its pretty interesting to see how each algorithm ‘broadens’ or ‘focuses’ the domain of its candidate solutions as iterations go by.

Baidu’s Deep Voice

In a 2-part series (Part 1 & Part 2), the author discusses the architecture of Baidu’s Text-to-Speech system (Deep Voice). Take a look if you have never read about/worked on such systems and want to have a general idea of how they are trained and deployed.

Capsule Networks

Geoff Hinton and his team at Google recently discussed the idea of Capsule networks, which try and remedy the rigidity in usual CNNs – by defining groups of specialized neurons called ‘capsules’ whose contribution to higher-level neurons is decided by the similarity of output. Heres a small intro on Capsule Networks, or the original paper if you wanna delve deeper.

Nexar Challenge Results

Nexar released the results of its Deep-Learning challenge on Image segmentation – the problem of ‘boxing’ and ‘tagging’ objects in pictures with multiple entities present. This is especially useful in their own AI-dashboard apps, which need to be quite accurate to prevent possible collisions in deployment.

As further reading, you could also check out this article on the history of CNNs in Image Segmentation, another one on Region-of-Interest Pooling in CNNs, and Deformable Neural Networks. (All of these concepts are mentioned in the main Nexar article)


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