The hypothesis in Ilyas et. al. is a special case of a more general principle that is well accepted in the distributional robustness literature — models lack robustness to distribution shift because they latch onto superficial correlations in the data. Naturally, the same principle also explains adversarial examples because they arise from a worst-case analysis of distribution […]
We want to thank all the commenters for the discussion and for spending time designing experiments analyzing, replicating, and expanding upon our results. These comments helped us further refine our understanding of adversarial examples (e.g., by visualizing useful non-robust features or illustrating how robust models are successful at downstream tasks), but also highlighted aspects of
On May 6th, Andrew Ilyas and colleagues published a paper outlining two sets of experiments. Firstly, they showed that models trained on adversarial examples can transfer to real data, and secondly that models trained on a dataset derived from the representations of robust neural networks seem to inherit non-trivial robustness. They proposed an intriguing interpretation
By some metrics, research on Generative Adversarial Networks (GANs) has progressed substantially in the past 2 years. Practical improvements to image synthesis models are being made almost too quickly to keep up with: Odena et al., 2016 Miyato et al., 2017 Zhang et al., 2018 Brock et al., 2018 However, by other metrics, less has
Even if you have spent some time reading about machine learning, chances are that you have never heard of Gaussian processes. And if you have, rehearsing the basics is always a good way to refresh your memory. With this blog post we want to give an introduction to Gaussian processes and make the mathematical intuition
Memorization in Recurrent Neural Networks (RNNs) continues to pose a challenge in many applications. We’d like RNNs to be able to store information over many timesteps and retrieve it when it becomes relevant — but vanilla RNNs often struggle to do this. Several network architectures have been proposed to tackle aspects of this problem, such as Long-Short-Term
Introduction Neural networks can learn to classify images more accurately than any system humans directly design. This raises a natural question: What have these networks learned that allows them to classify images so well? Feature visualization is a thread of research that tries to answer this question by letting us “see through the eyes” of
The goal of long-term artificial intelligence (AI) safety is to ensure that advanced AI systems are reliably aligned with human values — that they reliably do things that people want them to do.Roughly by human values we mean whatever it is that causes people to choose one option over another in each case, suitably corrected by reflection,
A little over a year ago, we formally launched Distill as an open-access scientific journal.Distill operated informally for several months before launching itself as a journal. It’s been an exciting ride since then! To give some very concrete metrics, Distill has had over a million unique readers, and more than 2.9 million views. Distill papers
Neural networks trained to classify images have a remarkable — and surprising! — capacity to generate images. Techniques such as DeepDream , style transfer, and feature visualization leverage this capacity as a powerful tool for exploring the inner workings of neural networks, and to fuel a small artistic movement based on neural art. All these techniques work in roughly
