How is neural architecture search performed? I have come across something that IBM offers called neural Architecture search. You feed it a data set and it outputs an initial neural Architecture that you can train. 

How is neural architecture search (NAS) performed? Do they use heuristics or is this meta machine learning?

If you have any papers on NAS, I would appreciate if you can provide a link to them.
You could say that NAS fits into the domain of Meta Learning or Meta Machine learning.

I've pulled the NAS papers from my notes, this is a collection of papers/lectures that I personally found very interesting. It's sorted in rough chronological descending order, and *** means influential / must read.

Quoc V. Le and Barret Zoph are to good authors on the topic.


The Evolved Transformer
Exploring Randomly Wired Neural Networks for Image Recognition
GRAPH HYPERNETWORKS FOR NEURAL ARCHITECTURE SEARCH
Backprop Evolution
Progressive Neural Architecture Search
*** DARTS: Differentiable Architecture Search
*** Efficient Neural Architecture Search via Parameter Sharing - ENAS
*** Progressive Neural Architecture Search
AlphaX: eXploring Neural Architectures with Deep Neural Networks and Monte Carlo Tree Search
Automatic Machine Learning - Prof. Frank Hutter
Google Brain - Neural Architecture Search - Quoc Le
*** Regularized Evolution for Image Classifier Architecture Search
Autostacker: A Compositional Evolutionary Learning System
Generating Neural Networks with Neural Networks
Finding Competitive Network Architectures Within a Day Using UCT
Neuroevolution: A different kind of deep learning
Evolving Deep Neural Networks
Pieter Abbeel: Deep Learning-to-Learn Robotic Control
*** SMASH: One-Shot Model Architecture Search through HyperNetworks