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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 .
Jul 13, 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 12, 2019 Multilingual Universal Sentence Encoder for Semantic Retrieval
Posted by Yinfei Yang and Amin Ahmad, Software Engineers, Google Research Since it was introduced last year , “ Universal Sentence Encoder (USE) for English ’’ has become one of the most downloaded pre-trained text modules in Tensorflow Hub , providing versatile sentence embedding models that convert sentences into vector representations.
Jun 25, 2019 Innovations in Graph Representation Learning
Posted by Alessandro Epasto, Senior Research Scientist and Bryan Perozzi, Senior Research Scientist, Graph Mining Team Relational data representing relationships between entities is ubiquitous on the Web (e.g., online social networks) and in the physical world (e.g., in protein interaction networks). Such data can be represented as a graph with nodes (e.g., users, proteins), and edges connecting them (e.g., friendship relations , protein interactions).
Jun 19, 2019 Off-Policy Classification - A New Reinforcement Learning Model Selection Method
Posted by Alex Irpan, Software Engineer, Robotics at Google Reinforcement learning (RL) is a framework that lets agents learn decision making from experience. One of the many variants of RL is off-policy RL, where an agent is trained using a combination of data collected by other agents (off-policy data) and data it collects itself to learn generalizable skills like robotic walking and grasping .
Jun 18, 2019 Google at CVPR 2019
Posted by Andrew Helton, Editor, Google AI Communications This week, Long Beach, CA hosts the 2019 Conference on Computer Vision and Pattern Recognition (CVPR 2019), 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 2019—over 250 Googlers will be in attendance to present papers and ...
Jun 12, 2019 Google at ICML 2019
Posted by Andrew Helton, Editor, Google AI Communications Machine learning 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 ...
Jun 07, 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 .
Jun 05, 2019 An Inside Look at Google Earth Timelapse
Posted by Paul Dille, Senior Software Developer, Carnegie Mellon University CREATE Lab, and Chris Herwig, Geo Data Engineer, Google Earth Outreach Six years ago, we first introduced Google Earth Timelapse , a global, zoomable time-lapse video that lets anyone explore our changing planet’s surface—from the global scale to the local scale.
May 29, 2019 EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling
Posted by Mingxing Tan, Staff Software Engineer and Quoc V. Le, Principal Scientist, Google AI Convolutional neural networks (CNNs) are commonly developed at a fixed resource cost, and then scaled up in order to achieve better accuracy when more resources are made available. For example, ResNet can be scaled up from ResNet-18 to ResNet-200 by increasing the number of layers, and recently, GPipe achieved 84.3% ImageNet top-1 accuracy by scaling up a ...
May 23, 2019 Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction
Posted by Tali Dekel, Research Scientist and Forrester Cole, Software Engineer, Machine Perception The human visual system has a remarkable ability to make sense of our 3D world from its 2D projection. Even in complex environments with multiple moving objects, people are able to maintain a feasible interpretation of the objects’ geometry and depth ordering.
May 15, 2019 Introducing Translatotron: An End-to-End Speech-to-Speech Translation Model
Posted by Ye Jia and Ron Weiss, Software Engineers, Google AI Speech-to-speech translation systems have been developed over the past several decades with the goal of helping people who speak different languages to communicate with each other. Such systems have usually been broken into three separate components: automatic speech recognition to transcribe the source speech as text, machine translation to translate the transcribed text into the target language, and text-to-speech synthesis (TTS) ...