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Apr 02, 2020 Announcing the 2020 Image Matching Benchmark and Challenge
Posted by Eduard Trulls, Research Scientist, Google Maps Reconstructing 3D objects and buildings from a series of images is a well-known problem in computer vision, known as Structure-from-Motion (SfM). It has diverse applications in photography and cultural heritage preservation (e.g., allowing people to explore the sculptures of Rapa Nui in a browser ) and powers many services across Google Maps, such as the 3D models created from StreetView and aerial imagery .
Apr 02, 2020 A Step Towards Protecting Patients from Medication Errors
Posted by Kathryn Rough, Research Scientist and Alvin Rajkomar, MD, Google Health While no doctor, nurse, or pharmacist wants to make a mistake that harms a patient, research shows that 2% of hospitalized patients experience serious preventable medication-related incidents that can be life-threatening, cause permanent harm, or result in death.
Apr 01, 2020 Improving Audio Quality in Duo with WaveNetEQ
Posted by Pablo Barrera, Software Engineer, Google Research and Florian Stimberg, Research Engineer, DeepMind Online calls have become an everyday part of life for millions of people by helping to streamline their work and connect them to loved ones. To transmit a call across the internet, the data from calls are split into short chunks, called packets.
Mar 30, 2020 A Neural Weather Model for Eight-Hour Precipitation Forecasting
Posted by Nal Kalchbrenner and Casper Sønderby, Research Scientists, Google Research, Amsterdam Predicting weather from minutes to weeks ahead with high accuracy is a fundamental scientific challenge that can have a wide ranging impact on many aspects of society. Current forecasts employed by many meteorological agencies are based on physical models of the atmosphere that, despite improving substantially over the preceding decades, are inherently constrained by their computational requirements and are sensitive ...
Mar 26, 2020 Exploring New Ways to Support Faculty Research
Posted by Maggie Johnson, VP, Google Research For the past 15 years, the Google Faculty Research Award Program has helped support world-class technical research in computer science, engineering, and related fields, funding over 2000 academics at ~400 Universities in 50+ countries since its inception. As Google Research continues to evolve, we continually explore new ways to improve our support of the broader research community, specifically on how to support new faculty while ...
Mar 23, 2020 Massively Scaling Reinforcement Learning with SEED RL
Posted by Lasse Espeholt, Research Engineer, Google Research, Amsterdam Reinforcement learning (RL) has seen impressive advances over the last few years as demonstrated by the recent success in solving games such as Go and Dota 2 . Models, or agents, learn by exploring an environment, such as a game, while optimizing for specified goals.
Mar 20, 2020 Visual Transfer Learning for Robotic Manipulation
Posted by Yen-Chen Lin, Research Intern and Andy Zeng, Research Scientist, Robotics at Google The idea that robots can learn to directly perceive the affordances of actions on objects (i.e., what the robot can or cannot do with an object) is called affordance-based manipulation , explored in research on learning complex vision-based manipulation skills including grasping , pushing , and throwing .
Mar 18, 2020 Introducing Dreamer: Scalable Reinforcement Learning Using World Models
Posted by Danijar Hafner, Student Researcher, Google Research Research into how artificial agents can choose actions to achieve goals is making rapid progress in large part due to the use of reinforcement learning (RL). Model-free approaches to RL, which learn to predict successful actions through trial and error, have enabled DeepMind's DQN to play Atari games and AlphaStar to beat world champions at Starcraft II, but require large amounts of environment interaction, ...
Mar 13, 2020 Fast and Easy Infinitely Wide Networks with Neural Tangents
Posted by Samuel S. Schoenholz, Senior Research Scientist and Roman Novak, Research Engineer, Google Research The widespread success of deep learning across a range of domains such as natural language processing , conversational agents , and connectomics , has transformed the landscape of research in machine learning and left researchers with a number of interesting and important open questions such as: Why do deep neural networks (DNNs) generalize so well despite being ...
Mar 12, 2020 Soli Radar-Based Perception and Interaction in Pixel 4
Posted by Jaime Lien, Research Engineer and Nicholas Gillian, Software Engineer, Google Advanced Technology and Projects The Pixel 4 and Pixel 4 XL are optimized for ease of use, and a key feature helping to realize this goal is Motion Sense , which enables users to interact with their Pixel in numerous ways without touching the device.
Mar 11, 2020 Real-Time 3D Object Detection on Mobile Devices with MediaPipe
Posted by Adel Ahmadyan and Tingbo Hou, Software Engineers, Google Research Object detection is an extensively studied computer vision problem, but most of the research has focused on 2D object prediction . While 2D prediction only provides 2D bounding boxes, by extending prediction to 3D, one can capture an object’s size, position and orientation in the world, leading to a variety of applications in robotics, self-driving vehicles, image retrieval, and augmented reality.
Mar 10, 2020 More Efficient NLP Model Pre-training with ELECTRA
Posted by Kevin Clark, Student Researcher and Thang Luong, Senior Research Scientist, Google Research, Brain Team Recent advances in language pre-training have led to substantial gains in the field of natural language processing, with state-of-the-art models such as BERT , RoBERTa , XLNet , ALBERT , and T5 , among many others.
Mar 09, 2020 Announcing TensorFlow Quantum: An Open Source Library for Quantum Machine Learning
Posted by Alan Ho, Product Lead and Masoud Mohseni, Technical Lead, Google Research “Nature isn’t classical, damnit, so if you want to make a simulation of nature, you’d better make it quantum mechanical.” — Physicist Richard Feynman Machine learning (ML), while it doesn’t exactly simulate systems in nature, has the ability to learn a model of a system and predict the system’s behavior.
Mar 06, 2020 Measuring Compositional Generalization
Posted by Marc van Zee, Software Engineer, Google Research People are capable of learning the meaning of a new word and then applying it to other language contexts. As Lake and Baroni put it, “Once a person learns the meaning of a new verb ‘dax’, he or she can immediately understand the meaning of ‘dax twice’ and ‘sing and dax’.” Similarly, one can learn a new object shape and then recognize it ...
Mar 03, 2020 Toward Human-Centered Design for ML Frameworks
Posted by Carrie J. Cai, Senior Research Scientist, Google Research and Philip J. Guo, Assistant Professor, UC San Diego As machine learning (ML) increasingly impacts diverse stakeholders and social groups, it has become necessary for a broader range of developers — even those without formal ML training — to be able to adapt and apply ML to their own problems.
Feb 28, 2020 Ultra-High Resolution Image Analysis with Mesh-TensorFlow
Posted by Le Hou and Youlong Cheng, Software Engineers, Google Research Deep neural network models form the backbone of most state-of-the-art image analysis and natural language processing algorithms. With the recent development of large-scale deep learning techniques such as data and model parallelism, large convolutional neural network (CNN) models can be trained on datasets of millions of images in minutes.
Feb 26, 2020 Setting Fairness Goals with the TensorFlow Constrained Optimization Library
Posted by Andrew Zaldivar, Responsible AI Advocate, Google Research, on behalf of the TFCO Team Many technologies that use supervised machine learning are having an increasingly positive impact on peoples’ day-to-day lives, from catching early signs of illnesses to filtering inappropriate content. There is, however, a growing concern that learned models, which generally satisfy the narrow requirement of minimizing a single loss function , may have difficulty addressing broader societal issues such ...
Feb 26, 2020 Open Images V6 — Now Featuring Localized Narratives
Posted by Jordi Pont-Tuset, Research Scientist, Google Research Open Images is the largest annotated image dataset in many regards, for use in training the latest deep convolutional neural networks for computer vision tasks. With the introduction of version 5 last May, the Open Images dataset includes 9M images annotated with 36M image-level labels, 15.8M bounding boxes, 2.8M instance segmentations, and 391k visual relationships.
Feb 25, 2020 Enhancing the Research Community’s Access to Street View Panoramas for Language Grounding Tasks
Posted by Harsh Mehta, Software Engineer and Jason Baldridge, Research Scientist, Google Research Significant advances continue to be made in both natural language processing and computer vision , but the research community is still far from having computer agents that can interpret instructions in a real-world visual context and take appropriate actions based on those instructions.
Feb 24, 2020 Google at NeurIPS 2019
Posted by Andrew Helton, Editor, Google Research Communications This week, Vancouver hosts the 33rd annual Conference on Neural Information Processing Systems (NeurIPS 2019), the biggest machine learning conference of the year. The conference includes invited talks, demonstrations and presentations of some of the latest in machine learning research.