Big Picture: Google Visualization Research
Big Picture is now part of Google's People + AI Research. This site is no longer maintained-- please see the PAIR site for our current work. We explore how information visualization can make complex data accessible, useful, and even fun. Our focus is on ways to illuminate the data and algorithms used in machine intelligence.
YouTube-BB Dataset | Google Research
YouTube-BoundingBoxes is a large-scale data set of video URLs with densely-sampled high-quality single-object bounding box annotations. The data set consists of approximately 380,000 15-20s video segments extracted from 240,000 different publicly visible YouTube videos, automatically selected to feature objects in natural settings without editing or post-processing, with a recording quality often
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.......................................................................................................................................................................................................................... GOOGLE-WIDE PROFILING: A CONTINUOUS PROFILING INFRASTRUCTURE FOR DATA CENTERS .......................................................................................................
Large Scale Distributed Deep Networks
Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines
Building high-level features using large scale unsupervised learning
We consider the problem of building highlevel, class-specific feature detectors from only unlabeled data. For example, is it possible to learn a face detector using only unlabeled images? To answer this, we train a 9-layered locally connected sparse autoencoder with pooling and local contrast normalization on a large dataset of images (the model has 1 billion connections, the dataset has 10 million
Jeffrey Dean
I joined Google in mid-1999, and I'm currently Google's Chief Scientist, focusing on AI advances for Google DeepMind and Google Research. My areas of focus include machine learning and AI and applications of AI to problems that help billions of people in societally beneficial ways. I have a broad variety of interests, including machine learning, large-scale distributed systems, computer systems pe
Spanner: Google's Globally-Distributed Database
Spanner is Google's scalable, multi-version, globally-distributed, and synchronously-replicated database. It is the first system to distribute data at global scale and support externally-consistent distributed transactions. This paper describes how Spanner is structured, its feature set, the rationale underlying various design decisions, and a novel time API that exposes clock uncertainty. This AP
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Life on the Edge: Monitoring and Running A Very Large Perforce Installation
https://research.google.com/pubs/archive/45687.pdf
Spanner, TrueTime & The CAP Theorem
Large Scale Distributed Deep Networks
Publications – Google Research
http://research.google.com/pubs/archive/42237.pdf
Publications – Google Research
Interpreting the Data: Parallel Analysis with Sawzall
http://research.google.com/pubs/archive/39959.pdf
Machine Learning: The High-Interest Credit Card of Technical Debt
Microsoft Word - querylogs_2.doc
Publications – Google Research
http://research.google.com/pubs/archive/40671.pdf