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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 .
Jul 24, 2019 Learning Better Simulation Methods for Partial Differential Equations
Posted by Stephan Hoyer, Software Engineer, Google Research The world’s fastest supercomputers were designed for modeling physical phenomena, yet they still are not fast enough to robustly predict the impacts of climate change , to design controls for airplanes based on airflow or to accurately simulate a fusion reactor .
Jul 19, 2019 Parrotron: New Research into Improving Verbal Communication for People with Speech Impairments
Posted by Fadi Biadsy, Research Scientist and Ron Weiss, Software Engineer, Google Research Most people take for granted that when they speak, they will be heard and understood. But for the millions who live with speech impairments caused by physical or neurological conditions, trying to communicate with others can be difficult and lead to frustration.
Jul 19, 2019 Announcing Open Images V5 and the ICCV 2019 Open Images Challenge
Posted by Vittorio Ferrari, Research Scientist, Machine Perception In 2016, we introduced Open Images , a collaborative release of ~9 million images annotated with labels spanning thousands of object categories. Since then we have rolled out several updates , culminating with Open Images V4 in 2018.
Jul 19, 2019 Introducing TensorNetwork, an Open Source Library for Efficient Tensor Calculations
Posted by Chase Roberts, Research Engineer, Google AI and Stefan Leichenauer, Research Scientist, X Many of the world's toughest scientific challenges, like developing high-temperature superconductors and understanding the true nature of space and time , involve dealing with the complexity of quantum systems. What makes these challenges difficult is that the number of quantum states in these systems is exponentially large, making brute-force computation infeasible.
Jul 19, 2019 Announcing Google-Landmarks-v2: An Improved Dataset for Landmark Recognition & Retrieval
Posted by Bingyi Cao and Tobias Weyand, Software Engineers, Google AI Last year we released Google-Landmarks , the largest world-wide landmark recognition dataset available at that time. In order to foster advancements in research on instance-level recognition (recognizing specific instances of objects, e.g. distinguishing Niagara Falls from just any waterfall) and image retrieval (matching a specific object in an input image to all other instances of that object in a catalog of ...
Jul 19, 2019 Applying AutoML to Transformer Architectures
Posted by David So, Software Engineer, Google AI Since it was introduced a few years ago, Google’s Transformer architecture has been applied to challenges ranging from generating fantasy fiction to writing musical harmonies . Importantly, the Transformer’s high performance has demonstrated that feed forward neural networks can be as effective as recurrent neural networks when applied to sequence tasks, such as language modeling and translation.
Jul 19, 2019 Predicting Bus Delays with Machine Learning
Posted by Alex Fabrikant, Research Scientist, Google Research Hundreds of millions of people across the world rely on public transit for their daily commute, and over half of the world's transit trips involve buses. As the world's cities continue growing, commuters want to know when to expect delays, especially for bus rides, which are prone to getting held up by traffic.
Jul 19, 2019 Predicting the Generalization Gap in Deep Neural Networks
Posted by Yiding Jiang, Google AI Resident Deep neural networks (DNN) are the cornerstone of recent progress in machine learning, and are responsible for recent breakthroughs in a variety of tasks such as image recognition , image segmentation , machine translation and more. However, despite their ubiquity, researchers are still attempting to fully understand the underlying principles that govern them.
Jul 19, 2019 Announcing the YouTube-8M Segments Dataset
Posted by Joonseok Lee and Joe Yue-Hei Ng, Software Engineers, Google Research Over the last two years, the First and Second YouTube-8M Large-Scale Video Understanding Challenge and Workshop have collectively drawn 1000+ teams from 60+ countries to further advance large-scale video understanding research. While these events have enabled great progress in video classification, the YouTube dataset on which they were based only used machine-generated video-level labels, and lacked fine-grained temporally localized information, ...
Jul 19, 2019 Building SMILY, a Human-Centric, Similar-Image Search Tool for Pathology
Posted by Narayan Hegde, Software Engineer, Google Health and Carrie J. Cai, Research Scientist, Google Research Advances in machine learning (ML) have shown great promise for assisting in the work of healthcare professionals, such as aiding the detection of diabetic eye disease and metastatic breast cancer .