Model evaluation, model selection, and algorithm selection in machine learning
Almost every machine learning algorithm comes with a large number of settings that we, the machine learning researchers and practitioners, need to specify…
Attention and Augmented Recurrent Neural Networks
A visual overview of neural attention, and the powerful extensions of neural networks being built on top of it.
Infrastructure for deep learning
Deep learning is an empirical science, and the quality of a group’s infrastructure is a multiplier on progress. Fortunately, today’s open-source ecosystem makes it possible for anyone to build great deep learning infrastructure.
Machine Learning Unconference
The latest information about the Unconference is now available at the Unconference wiki, which will be periodically updated with more information for attendees.
Model evaluation, model selection, and algorithm selection in machine learning
In this second part of this series, we will look at some advanced techniques for model evaluation and techniques to estimate the uncertainty of our…
Special projects
Impactful scientific work requires working on the right problems—problems which are not just interesting, but whose solutions matter.
Concrete AI safety problems
We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems opera…
OpenAI technical goals
OpenAI’s mission is to build safe AI, and ensure AI’s benefits are as widely and evenly distributed as possible.