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Sep 13, 2017 The Google Brain Team’s Approach to Research
Posted by Jeff Dean, Google Senior Fellow About a year ago, the Google Brain team first shared our mission “Make machines intelligent. Improve people’s lives.” In that time, we’ve shared updates on our work to infuse machine learning across Google products that hundreds of millions of users access everyday, including Translate , Maps , and more.
Sep 12, 2017 Highlights from the Annual Google PhD Fellowship Summit, and Announcing the 2017 Google PhD Fellows
Posted by Susie Kim, Program Manager, University Relations In 2009, Google created the PhD Fellowship Program to recognize and support outstanding graduate students doing exceptional research in Computer Science and related disciplines. Now in its ninth year, our Fellowships have helped support over 300 graduate students in Australia , China and East Asia , India , North America, Europe and the Middle East who seek to shape and influence the future of ...
Sep 12, 2017 Google at KDD’17: Graph Mining and Beyond
Posted by Bryan Perozzi, Research Scientist, NYC Algorithms and Optimization Team The 23rd ACM conference on Knowledge Discovery and Data Mining (KDD’17), a main venue for academic and industry research in data science, information retrieval, data mining and machine learning, was held last week in Halifax, Canada.
Sep 11, 2017 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 changes over time or exploring clusters in high-dimensional spaces .
Sep 08, 2017 Harness the Power of Machine Learning in Your Browser with Deeplearn.js
Posted by Nikhil Thorat and Daniel Smilkov, Software Engineers, Google Big Picture Team Machine learning (ML) has become an increasingly powerful tool, one that can be applied to a wide variety of areas spanning object recognition , language translation , health and more. However, the development of ML systems is often restricted to those with computational resources and the technical expertise to work with commonly available ML libraries.
Sep 06, 2017 Seminal Ideas from 2007
Posted by Anna Ukhanova, Technical Program Manager, Google Research Europe It is not everyday we have the chance to pause and think about how previous work has led to current successes, how it influenced other advances and reinterpret it in today’s context. That’s what the ICML Test-of-Time Award is meant to achieve, and this year it was given to the work Sylvain Gelly , now a researcher on the Google Brain team ...
Sep 01, 2017 Transformer: A Novel Neural Network Architecture for Language Understanding
Posted by Jakob Uszkoreit, Software Engineer, Natural Language Understanding Neural networks, in particular recurrent neural networks (RNNs), are now at the core of the leading approaches to language understanding tasks such as language modeling , machine translation and question answering . In Attention Is All You Need we introduce the Transformer, a novel neural network architecture based on a self-attention mechanism that we believe to be particularly well-suited for language understanding.
Aug 25, 2017 Exploring and Visualizing an Open Global Dataset
Posted by Reena Jana, Creative Lead, Business Inclusion, and Josh Lovejoy, UX Designer, Google Research Machine learning systems are increasingly influencing many aspects of everyday life, and are used by both the hardware and software products that serve people globally. As such, researchers and designers seeking to create products that are useful and accessible for everyone often face the challenge of finding data sets that reflect the variety and backgrounds of users ...
Aug 24, 2017 Launching the Speech Commands Dataset
Posted by Pete Warden, Software Engineer, Google Brain Team At Google, we’re often asked how to get started using deep learning for speech and other audio recognition problems, like detecting keywords or commands. And while there are some great open source speech recognition systems like Kaldi that can use neural networks as a component, their sophistication makes them tough to use as a guide to a simpler tasks.
Aug 23, 2017 Expressions in Virtual Reality
Posted by Steven Hickson, Software Engineering Intern, and Nick Dufour, Avneesh Sud, Software Engineers, Machine Perception Recently Google Machine Perception researchers, in collaboration with Daydream Labs and YouTube Spaces , presented a solution for virtual headset ‘removal’ for mixed reality in order to create a more rich and engaging VR experience.
Aug 21, 2017 Announcing the NYC Algorithms and Optimization Site
Posted by Vahab Mirrokni, Principal Research Scientist and Xerxes Dotiwalla, Product Manager, NYC Algorithms and Optimization Team New York City is home to several Google algorithms research groups. We collaborate closely with the teams behind many Google products and work on a wide variety of algorithmic challenges, like optimizing infrastructure , protecting privacy , improving friend suggestions and much more.
Aug 18, 2017 Making Visible Watermarks More Effective
Posted by Tali Dekel and Michael Rubinstein, Research Scientists Whether you are a photographer, a marketing manager, or a regular Internet user, chances are you have encountered visible watermarks many times. Visible watermarks are those logos and patterns that are often overlaid on digital images provided by stock photography websites, marking the image owners while allowing viewers to perceive the underlying content so that they could license the images that fit their ...
Aug 16, 2017 Teaching Robots to Understand Semantic Concepts
Posted by Sergey Levine, Faculty Advisor and Pierre Sermanet, Research Scientist, Google Brain Team Machine learning can allow robots to acquire complex skills, such as grasping and opening doors . However, learning these skills requires us to manually program reward functions that the robots then attempt to optimize.
Aug 10, 2017 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 to solve deep scientific and engineering challenges in areas of language, speech, translation, music, visual ...
Aug 01, 2017 Google at ACL 2017
Posted by Christian Howard, Editor-in-Chief, Research Communications This week, Vancouver, Canada hosts the 2017 Annual Meeting of the Association for Computational Linguistics (ACL 2017), the premier conference in the field of natural language understanding , covering a broad spectrum of diverse research areas that are concerned with computational approaches to natural language.
Jul 28, 2017 Revisiting the Unreasonable Effectiveness of Data
Posted by Abhinav Gupta, Faculty Advisor, Machine Perception There has been remarkable success in the field of computer vision over the past decade, much of which can be directly attributed to the application of deep learning models to this machine perception task. Furthermore, since 2012 there have been significant advances in representation capabilities of these systems due to (a) deeper models with high complexity, (b) increased computational power and (c) availability of ...
Jul 25, 2017 So there I was, firing a megawatt plasma collider at work...
Posted by Ted Baltz, Senior Staff Software Engineer, Google Accelerated Science Team Wait, what? Why is Google interested in plasma physics? Google is always interested in solving complex engineering problems, and few are more complex than fusion . Physicists have been trying since the 1950s to control the fusion of hydrogen atoms into helium, which is the same process that powers the Sun.
Jul 21, 2017 Google at CVPR 2017
Posted by Christian Howard, Editor-in-Chief, Research Communications From July 21-26, Honolulu, Hawaii hosts the 2017 Conference on Computer Vision and Pattern Recognition (CVPR 2017), the premier annual computer vision event comprising the main conference and several co-located workshops and tutorials. As a leader in computer vision research and a Platinum Sponsor, Google will have a strong presence at CVPR 2017 — over 250 Googlers will be in attendance to present papers and ...
Jul 20, 2017 The Google Brain Residency Program — One Year Later
Posted by Luke Metz, Research Associate and Yun Liu, Software Engineer, 2016 Google Brain Resident Alumni “Coming from a background in statistics, physics, and chemistry, the Google Brain Residency was my first exposure to both deep learning and serious programming. I enjoyed the autonomy that I was given to research diverse topics of my choosing: deep learning for computer vision and language, reinforcement learning, and theory.
Jul 20, 2017 An Update to Open Images - Now with Bounding-Boxes
Posted by Vittorio Ferrari, Research Scientist, Machine Perception Last year we introduced Open Images , a collaborative release of ~9 million images annotated with labels spanning over 6000 object categories, designed to be a useful dataset for machine learning research. The initial release featured image-level labels automatically produced by a computer vision model similar to Google Cloud Vision API , for all 9M images in the training set, and a validation set ...