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Jan 22, 2020 Releasing the Drosophila Hemibrain Connectome — The Largest Synapse-Resolution Map of Brain Connectivity
Posted by Michal Januszewski, Software Engineer and Viren Jain, Research Scientist and Technical Lead, Connectomics at Google A fundamental way to describe a complex system is to measure its “network” — the way individual parts connect and communicate with each other. For example, biologists study gene networks , social scientists study social networks and even search engines rely, in part, on analyzing the way web pages form a network by linking to ...
Jan 16, 2020 Reformer: The Efficient Transformer
Posted by Nikita Kitaev, Student Researcher, UC Berkeley and Łukasz Kaiser, Research Scientist, Google Research Understanding sequential data — such as language, music or videos — is a challenging task, especially when there is dependence on extensive surrounding context. For example, if a person or an object disappears from view in a video only to re-appear much later, many models will forget how it looked.
Jan 16, 2020 Can You Trust Your Model’s Uncertainty?
Posted by Jasper Snoek, Research Scientist and Zachary Nado, Research Engineer, Google Research In an ideal world, machine learning (ML) methods like deep learning are deployed to make predictions on data from the same distribution as that on which they were trained. But the practical reality can be quite different: camera lenses becoming blurry, sensors degrading, and changes to popular online topics can result in differences between the distribution of data on ...
Jan 14, 2020 Using Machine Learning to “Nowcast” Precipitation in High Resolution
Posted by Jason Hickey, Senior Software Engineer, Google Research The weather can affect a person’s daily routine in both mundane and serious ways, and the precision of forecasting can strongly influence how they deal with it. Weather predictions can inform people about whether they should take a different route to work, if they should reschedule the picnic planned for the weekend, or even if they need to evacuate their homes due to ...
Jan 10, 2020 Google Research: Looking Back at 2019, and Forward to 2020 and Beyond
Posted by Jeff Dean, Senior Fellow and SVP of Google Research and Health, on behalf of the entire Google Research community The goal of Google Research is to work on long-term, ambitious problems, with an emphasis on solving ones that will dramatically help people throughout their daily lives.
Dec 20, 2019 ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations
Posted by Radu Soricut and Zhenzhong Lan, Research Scientists, Google Research Ever since the advent of BERT a year ago, natural language research has embraced a new paradigm, leveraging large amounts of existing text to pretrain a model’s parameters using self-supervision, with no data annotation required.
Dec 19, 2019 The On-Device Machine Learning Behind Recorder
Posted by Itay Inbar and Nir Shemy, Software Engineers, Google Research Over the past two decades, Google has made information widely accessible through search — from textual information, photos and videos, to maps and jobs. But much of the world’s information is conveyed through speech. Yet even though many people use audio recording devices to capture important information in conversations, interviews, lectures and more, it can be very difficult to later parse ...
Dec 17, 2019 New Solutions for Quantum Gravity with TensorFlow
Posted by Thomas Fischbacher, Software Engineer, Google Research, Zürich Recent strides in machine learning (ML) research have led to the development of tools useful for research problems well beyond the realm for which they were designed. The value of these tools when applied to topics ranging from teaching robots how to throw to predicting the olfactory properties of molecules is now beginning to be realized.
Dec 17, 2019 Improving Out-of-Distribution Detection in Machine Learning Models
Posted by Jie Ren, Research Scientist, Google Research and Balaji Lakshminarayanan, Research Scientist, DeepMind Successful deployment of machine learning systems requires that the system be able to distinguish between data that is anomalous or significantly different from that used in training. This is particularly important for deep neural network classifiers, which might classify such out-of-distribution (OOD) inputs into in-distribution classes with high confidence.
Dec 16, 2019 Improvements to Portrait Mode on the Google Pixel 4 and Pixel 4 XL
Posted by Neal Wadhwa, Software Engineer and Yinda Zhang, Research Scientist, Google Research Portrait Mode on Pixel phones is a camera feature that allows anyone to take professional-looking shallow depth of field images. Launched on the Pixel 2 and then improved on the Pixel 3 by using machine learning to estimate depth from the camera’s dual-pixel auto-focus system, Portrait Mode draws the viewer’s attention to the subject by blurring out the background.
Dec 11, 2019 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.
Dec 11, 2019 Fairness Indicators: Scalable Infrastructure for Fair ML Systems
Posted by Catherina Xu and Tulsee Doshi, Product Managers, Google Research While industry and academia continue to explore the benefits of using machine learning (ML) to make better products and tackle important problems, algorithms and the datasets on which they are trained also have the ability to reflect or reinforce unfair biases.
Dec 10, 2019 Lessons Learned from Developing ML for Healthcare
Posted by Yun Liu, Research Scientist and Po-Hsuan Cameron Chen, Research Engineer, Google Health Machine learning (ML) methods are not new in medicine -- traditional techniques, such as decision trees and logistic regression, were commonly used to derive established clinical decision rules (for example, the TIMI Risk Score for estimating patient risk after a coronary event).
Dec 06, 2019 Understanding Transfer Learning for Medical Imaging
Posted by Maithra Raghu and Chiyuan Zhang, Research Scientists, Google Research As deep neural networks are applied to an increasingly diverse set of domains, transfer learning has emerged as a highly popular technique in developing deep learning models. In transfer learning, the neural network is trained in two stages: 1) pretraining, where the network is generally trained on a large-scale benchmark dataset representing a wide diversity of labels/categories (e.g., ImageNet ); and ...
Dec 03, 2019 Astrophotography with Night Sight on Pixel Phones
Posted by Florian Kainz and Kiran Murthy, Software Engineers, Google Research Taking pictures of outdoor scenes at night has so far been the domain of large cameras, such as DSLRs, which are able to achieve excellent image quality, provided photographers are willing to put up with bulky equipment and sometimes tricky postprocessing.
Dec 03, 2019 Developing Deep Learning Models for Chest X-rays with Adjudicated Image Labels
Posted by Dave Steiner, MD, Research Scientist and Shravya Shetty, Technical Lead, Google Health With millions of diagnostic examinations performed annually, chest X-rays are an important and accessible clinical imaging tool for the detection of many diseases. However, their usefulness can be limited by challenges in interpretation, which requires rapid and thorough evaluation of a two-dimensional image depicting complex, three-dimensional organs and disease processes.
Nov 22, 2019 Google at ICCV 2019
Posted by Andrew Helton, Editor, Google Research Communications This week, Seoul, South Korea hosts the International Conference on Computer Vision 2019 (ICCV 2019), one of the world's premier conferences on computer vision. As a leader in computer vision research and a Gold Sponsor, Google will have a strong presence at ICCV 2019 with over 200 Googlers in attendance, more than 40 research presentations, and involvement in the organization of a number of ...
Nov 21, 2019 Introducing the Next Generation of On-Device Vision Models: MobileNetV3 and MobileNetEdgeTPU
Posted by Andrew Howard, Software Engineer and Suyog Gupta, Silicon Engineer, Google Research On-device machine learning (ML) is an essential component in enabling privacy-preserving, always-available and responsive intelligence. This need to bring on-device machine learning to compute and power-limited devices has spurred the development of algorithmically-efficient neural network models and hardware capable of performing billions of math operations per second, while consuming only a few milliwatts of power.
Nov 21, 2019 SPICE: Self-Supervised Pitch Estimation
Posted by Marco Tagliasacchi, Research Scientist, Google Research A sound’s pitch is a qualitative measure of its frequency, where a sound with a high pitch is higher in frequency than one of low pitch. Through tracking relative differences in pitch, our auditory system is able to recognize audio features, such as a song’s melody.
Nov 21, 2019 RecSim: A Configurable Simulation Platform for Recommender Systems
Posted by Martin Mladenov, Research Scientist and Chih-wei Hsu, Software Engineer, Google Research Significant advances in machine learning, speech recognition, and language technologies are rapidly transforming the way in which recommender systems engage with users. As a result, collaborative interactive recommenders (CIRs) — recommender systems that engage in a deliberate sequence of interactions with a user to best meet that user's needs — have emerged as a tangible goal for online services.