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Sep 18, 2019 Project Ihmehimmeli: Temporal Coding in Spiking Neural Networks
Posted by Iulia-Maria Comșa and Krzysztof Potempa, Research Engineers, Google Research, Zürich The discoveries being made regularly in neuroscience are an ongoing source of inspiration for creating more efficient artificial neural networks that process information in the same way as biological organisms. These networks have recently achieved resounding success in domains ranging from playing board and video games to fine-grained understanding of video .
Sep 13, 2019 Using Deep Learning to Inform Differential Diagnoses of Skin Diseases
Posted by Yuan Liu, PhD, Software Engineer and Peggy Bui, MD, Technical Program Manager, Google Health An estimated 1.9 billion people worldwide suffer from a skin condition at any given time, and due to a shortage of dermatologists, many cases are seen by general practitioners instead.
Sep 11, 2019 Learning Cross-Modal Temporal Representations from Unlabeled Videos
Posted by Chen Sun and Cordelia Schmid, Research Scientists, Google Research While people can easily recognize what activities are taking place in videos and anticipate what events may happen next, it is much more difficult for machines. Yet, increasingly, it is important for machines to understand the contents and dynamics of videos for applications, such as temporal localization , action detection and navigation for self-driving cars .
Sep 10, 2019 Recursive Sketches for Modular Deep Learning
Posted by Badih Ghazi and Joshua R. Wang, Research Scientists, Google Research Much of classical machine learning (ML) focuses on utilizing available data to make more accurate predictions. More recently, researchers have considered other important objectives, such as how to design algorithms to be small , efficient , and robust .
Sep 10, 2019 Joint Speech Recognition and Speaker Diarization via Sequence Transduction
Posted by Laurent El Shafey, Software Engineer and Izhak Shafran, Research Scientist, Google Health Being able to recognize “who said what,” or speaker diarization, is a critical step in understanding audio of human dialog through automated means. For instance, in a medical conversation between doctors and patients, “Yes” uttered by a patient in response to “Have you been taking your heart medications regularly?” has a substantially different implication than a rhetorical “Yes?” ...
Sep 10, 2019 Announcing Two New Natural Language Dialog Datasets
Posted by Bill Byrne and Filip Radlinski, Research Scientists, Google Research Today’s digital assistants are expected to complete tasks and return personalized results across many subjects, such as movie listings, restaurant reservations and travel plans. However, despite tremendous progress in recent years, they have not yet reached human-level understanding.
Sep 09, 2019 Announcement of the 2019 Fellowship Awardees and Highlights from the Google PhD Fellowship Summit
Posted by Susie Kim, Program Manager, University Relations In 2009, Google created the PhD Fellowship Program to recognize and support outstanding graduate students who are doing exceptional research in Computer Science and related fields who seek to influence the future of technology. Now in its eleventh year, these Fellowships have helped support 450 graduate students globally in North America and Europe, Australia, Asia, Africa and India.
Sep 04, 2019 Giving Lens New Reading Capabilities in Google Go
Posted by Rajan Patel, Director, Augmented Reality Around the world, millions of people are coming online for the first time, and many of them are among the 800 million adults worldwide who are unable to read or write, or those who are migrating to towns and cities where they are not able to speak the predominant language.
Sep 03, 2019 On-Device, Real-Time Hand Tracking with MediaPipe
Posted by Valentin Bazarevsky and Fan Zhang, Research Engineers, Google Research The ability to perceive the shape and motion of hands can be a vital component in improving the user experience across a variety of technological domains and platforms. For example, it can form the basis for sign language understanding and hand gesture control, and can also enable the overlay of digital content and information on top of the physical world in ...
Aug 29, 2019 Exploring Weight Agnostic Neural Networks
Posted by Adam Gaier, Student Researcher and David Ha, Staff Research Scientist, Google Research, Tokyo When training a neural network to accomplish a given task, be it image classification or reinforcement learning , one typically refines a set of weights associated with each connection within the network.
Aug 26, 2019 Google at ACL 2019
Andrew Helton, Editor, Google Research Communications This week, Florence, Italy hosts the 2019 Annual Meeting of the Association for Computational Linguistics (ACL 2019), the premier conference in the field of natural language understanding, covering a broad spectrum of research areas that are concerned with computational approaches to natural language.
Aug 26, 2019 Bi-Tempered Logistic Loss for Training Neural Nets with Noisy Data
Posted by Ehsan Amid, Student Researcher and Rohan Anil, Software Engineer, Google Research The quality of models produced by machine learning (ML) algorithms directly depends on the quality of the training data, but real world datasets typically contain some amount of noise that introduces challenges for ML models.
Aug 20, 2019 Turbo, An Improved Rainbow Colormap for Visualization
Posted by Anton Mikhailov, Senior Software Engineer, Daydream False color maps show up in many applications in computer vision and machine learning, from visualizing depth images to more abstract uses, such as image differencing . Colorizing images helps the human visual system pick out detail, estimate quantitative values, and notice patterns in data in a more intuitive fashion.
Aug 16, 2019 Robust Neural Machine Translation
Posted by Yong Cheng, Software Engineer, Google Research In recent years, neural machine translation (NMT) using Transformer models has experienced tremendous success. Based on deep neural networks , NMT models are usually trained end-to-end on very large parallel corpora (input/output text pairs) in an entirely data-driven fashion and without the need to impose explicit rules of language.
Aug 13, 2019 Project Euphonia’s Personalized Speech Recognition for Non-Standard Speech
Posted by Joel Shor and Dotan Emanuel, Research Engineers, Google Research, Tel Aviv The utility of technology is dependent on its accessibility. One key component of accessibility is automatic speech recognition (ASR), which can greatly improve the ability of those with speech impairments to interact with every-day smart devices.
Aug 08, 2019 An Interactive, Automated 3D Reconstruction of a Fly Brain
Posted by Peter H. Li, Research Scientist and Jeremy Maitin-Shepard, Software Engineer, Connectomics at Google The goal of connectomics research is to map the brain’s "wiring diagram" in order to understand how the nervous system works. A primary target of recent work is the brain of the fruit fly (Drosophila melanogaster), which is a well-established research animal in biology.
Aug 08, 2019 Video Understanding Using Temporal Cycle-Consistency Learning
Posted by Debidatta Dwibedi, Research Associate, Google Research In the last few years there has been great progress in the field of video understanding. For example, supervised learning and powerful deep learning models can be used to classify a number of possible actions in videos, summarizing the entire clip with a single label.
Aug 06, 2019 EfficientNet-EdgeTPU: Creating Accelerator-Optimized Neural Networks with AutoML
Posted by Suyog Gupta, Machine Learning Accelerator Architect and Mingxing Tan, Software Engineer, Google Research For several decades, computer processors have doubled their performance every couple of years by reducing the size of the transistors inside each chip, as described by Moore’s Law . As reducing transistor size becomes more and more difficult, there is a renewed focus in the industry on developing domain-specific architectures — such as hardware accelerators — to ...
Jul 30, 2019 Advancing Semi-supervised Learning with Unsupervised Data Augmentation
Posted by Qizhe Xie, Student Researcher and Thang Luong, Senior Research Scientist, Google Research, Brain Team Success in deep learning has largely been enabled by key factors such as algorithmic advancements, parallel processing hardware ( GPU / TPU ), and the availability of large-scale labeled datasets, like ImageNet .
Jul 26, 2019 Introducing Google Research Football: A Novel Reinforcement Learning Environment
Posted by Karol Kurach, Research Lead and Olivier Bachem, Research Scientist, Google Research, Zürich The goal of reinforcement learning (RL) is to train smart agents that can interact with their environment and solve complex tasks, with real-world applications towards robotics , self-driving cars , and more .