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Highlights from the Google AI Residency Program

Tuesday, October 9, 2018

Posted by Phing Lee, Program Manager, Google AI Residency
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Some of our 2017 Google AI residents at the 2017 Neural Information Processing Systems Conference, hosted in Long Beach, California.

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A study on the effect of adversarial examples on human visual perception.

An algorithm that enables robots to learn more safely by avoiding states from which they cannot reset.

Initialization methods which enable training of neural network with unprecedented depths of 10K+ layers.

A method to make training more scalable by using larger mini-batches, which when applied to ResNet-50 on ImageNet reduced training time without compromising test accuracy.

And many more...

This experiment demonstrated (for the first time) the susceptibility of human time-limited vision to adversarial examples. For more details, see “Adversarial Examples that Fool both Computer Vision and Time-Limited Humans” accepted at NIPS 2018).

An algorithm for safe reinforcement learning prevents robots from taking actions they cannot undo. For more details, see “Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning” (accepted at ICLR 2018).

Extremely deep CNNs can be trained without the use of any special tricks simply by using a specially designed (Delta-Orthogonal) initialization. Test (solid) and training (dashed) curves on MNIST (top) and CIFAR10 (bottom). For more details, see “Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks” accepted at ICML 2018.

Applying a sequence of simple scaling rules, we increase the SGD batch size and reduce the number of parameter updates required to train our model by an order of magnitude, without sacrificing test set accuracy. This enables us to dramatically reduce model training time. For more details, see “Don’t Decay the Learning Rate, Increase the Batch Size”, accepted at ICLR 2018.