Quoc Le at AI Frontiers : Automated Machine Learning

Nov 13, 2018Download as pptx, pdf1 like1,316 views

Traditional machine learning systems are hand-designed and tuned by machine learning experts. To scale up the impact of machine learning to many real-world applications, we must figure out a way to automate the designing process of these pipelines. In this talk, I will discuss the use of machine learning to automate the process of designing neural architectures and data augmentation strategies (Neural Architecture Search and AutoAugment).

AutoML - Automated Machine Learning
Quoc Le
@quocleix
Joint work with many colleagues in the Google Brain team

Current:
Solution =
ML Expertise + Data + Computation

Current:
Solution =
ML Expertise + Data + Computation
But can we turn this into:
Solution =
Data + 100X Computation

Data processing
Machine
Learning Model
Data

Importance of architectures for Vision
● Designing neural network architectures is hard
● Lots of human efforts go into tuning them
● Can we try and learn good architectures automatically?
Canziani et al, 2017

Convolutional Architectures
Krizhevsky et al, 2012

Neural Architecture Search
● We can specify the structure and connectivity of a neural network by a
configuration string
○ [“Filter Width: 5”, “Filter Height: 3”, “Num Filters: 24”]
● Use a RNN (“Controller”) to generate this string (the “Child Network”)
● Train the Child Network to see how well it performs on a validation set
● Use reinforcement learning to update the Controller based on the accuracy of
the Child Network

Controller: proposes Child Networks Train & evaluate Child Networks
20K
times
Iterate to
find the
most
accurate
Child
Network

Controller: proposes Child Networks Train & evaluate Child Networks
20K
times
Iterate to
find the
most
accurate
Child
Network
Reinforcement Learning
or Evolution Search

Neural Architecture Search for Convolutional Networks
Controller RNN
Softmax classifier
Embedding

Confidential + Proprietary
Strange hump
Basically
linear

Confidential + Proprietary
Mobile NASNet-A on ImageNet

Data processing
Machine
Learning Model
Data
Focus of machine
learning research

Data processing
Machine
Learning Model
Data
Focus of machine
learning research
Very important but
manually tuned

AutoAugment: Example Policy
Probability of applying
Magnitude

CIFAR-10
State-of-art: 2.1% error
AutoAugment: 1.5% error
ImageNet
State-of-art: 3.9% error
AutoAugment: 3.5% error

ImageNet
AutoML
Top-1Accuracy
ML Experts

References
● Neural Architecture Search with Reinforcement Learning. Barret Zoph and Quoc
V. Le. ICLR, 2017
● Learning Transferable Architectures for Large Scale Image Recognition. Barret
Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le. CVPR, 2018
● AutoAugment: Learning Augmentation Policies from Data. Ekin D. Cubuk, Barret
Zoph, Dandelion Mane, Vijay Vasudevan, Quoc V. Le. Arxiv, 2018
● Searching for Activation Functions. Prajit Ramachandran, Barret Zoph, Quoc Le.
ICLR Workshop, 2018

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