Google AI Blog: The Google Brain Team — Looking Back on 2017 (Part 2 of 2)
Posted by Jeff Dean, Google Senior Fellow, on behalf of the entire Google Brain Team The Google Brain team works to advance the state of the art in artificial intelligence by research and systems engineering, as one part of the overall Google AI effort. In Part 1 of this blog post, we shared some of our work in 2017 related to our broader research, from designing new machine learning algorithms an
The Google Brain Team — Looking Back on 2017 (Part 1 of 2)
Posted by Jeff Dean, Google Senior Fellow, on behalf of the entire Google Brain Team The Google Brain team works to advance the state of the art in artificial intelligence by research and systems engineering, as one part of the overall Google AI effort. Last year we shared a summary of our work in 2016. Since then, we’ve continued to make progress on our long-term research agenda of making machine
Build your own Machine Learning Visualizations with the new TensorBoard API
Posted by Chi Zeng and Justine Tunney, Software Engineers, Google Brain Team When we open-sourced TensorFlow in 2015, it included TensorBoard, a suite of visualizations for inspecting and understanding your TensorFlow models and runs. Tensorboard included a small, predetermined set of visualizations that are generic and applicable to nearly all deep learning applications such as observing how loss
Google at ICML 2017
Posted by Christian Howard, Editor-in-Chief, Research Communications Machine learning (ML) is a key strategic focus at Google, with highly active groups pursuing research in virtually all aspects of the field, including deep learning and more classical algorithms, exploring theory as well as application. We utilize scalable tools and architectures to build machine learning systems that enable us t
MultiModel: Multi-Task Machine Learning Across Domains
Posted by Łukasz Kaiser, Senior Research Scientist, Google Brain Team and Aidan N. Gomez, Researcher, Department of Computer Science Machine Learning Group, University of Toronto Over the last decade, the application and performance of Deep Learning has progressed at an astonishing rate. However, the current state of the field is that the neural network architectures are highly specialized to spec
Accelerating Deep Learning Research with the Tensor2Tensor Library
As an example of the kind of improvements T2T can offer, we applied the library to machine translation. As you can see in the table above, two different T2T models, SliceNet and Transformer, outperform the previous state-of-the-art, GNMT+MoE. Our best T2T model, Transformer, is 3.8 points better than the standard GNMT model, which itself was 4 points above the baseline phrase-based translation sys
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Introducing the CVPR 2018 On-Device Visual Intelligence Challenge
Research Blog: Seeing More with In Silico Labeling of Microscopy Images
Mobile Real-time Video Segmentation
Introducing the HDR+ Burst Photography Dataset
The Instant Motion Tracking Behind Motion Stills AR
Evaluation of Speech for the Google Assistant
Fused Video Stabilization on the Pixel 2 and Pixel 2 XL
Seamless Google Street View Panoramas
Tangent: Source-to-Source Debuggable Derivatives
Using Deep Learning to Create Professional-Level Photographs
Revisiting the Unreasonable Effectiveness of Data
The Google Brain Residency Program — One Year Later
Introducing the TensorFlow Research Cloud
Using Machine Learning to Explore Neural Network Architecture
