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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 14, 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.
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.
Jun 04, 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.
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) ...
May 09, 2019 An End-to-End AutoML Solution for Tabular Data at KaggleDays
Posted by Yifeng Lu, Software Engineer, Google AI Machine learning (ML) for tabular data (e.g. spreadsheet data) is one of the most active research areas in both ML research and business applications. Solutions to tabular data problems, such as fraud detection and inventory prediction, are critical for many business sectors, including retail, supply chain, finance, manufacturing, marketing and others.
May 09, 2019 Google at ICLR 2019
Posted by Andrew Helton, Editor, Google AI Communications This week, New Orleans, LA hosts the 7th International Conference on Learning Representations ( ICLR 2019 ), a conference focused on how one can learn meaningful and useful representations of data for machine learning . ICLR offers conference and workshop tracks, both of which include invited talks along with oral and poster presentations of some of the latest research on deep learning , metric ...
May 08, 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.
May 03, 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 ...
Apr 29, 2019 Announcing the 6th Fine-Grained Visual Categorization Workshop
Posted by Christine Kaeser-Chen, Software Engineer and Serge Belongie, Visiting Faculty, Google AI In recent years, fine-grained visual recognition competitions (FGVCs), such as the iNaturalist species classification challenge and the iMaterialist product attribute recognition challenge, have spurred progress in the development of image classification models focused on detection of fine-grained visual details in both natural and man-made objects.
Apr 24, 2019 Evaluating the Unsupervised Learning of Disentangled Representations
Posted by Olivier Bachem, Research Scientist, Google AI Zürich The ability to understand high-dimensional data, and to distill that knowledge into useful representations in an unsupervised manner, remains a key challenge in deep learning . One approach to solving these challenges is through disentangled representations, models that capture the independent features of a given scene in such a way that if one feature changes, the others remain unaffected.
Apr 22, 2019 SpecAugment: A New Data Augmentation Method for Automatic Speech Recognition
Posted by Daniel S. Park, AI Resident and William Chan, Research Scientist Automatic Speech Recognition (ASR), the process of taking an audio input and transcribing it to text, has benefited greatly from the ongoing development of deep neural networks . As a result, ASR has become ubiquitous in many modern devices and products, such as Google Assistant, Google Home and YouTube.
Apr 17, 2019 MorphNet: Towards Faster and Smaller Neural Networks
Posted by Andrew Poon, Senior Software Engineer and Dhyanesh Narayanan, Product Manager, Google AI Perception Deep neural networks (DNNs) have demonstrated remarkable effectiveness in solving hard problems of practical relevance such as image classification , text recognition and speech transcription . However, designing a suitable DNN architecture for a given problem continues to be a challenging task.
Apr 16, 2019 Take Your Best Selfie Automatically, with Photobooth on Pixel 3
Posted by Navid Shiee, Senior Software Engineer and Aseem Agarwala, Staff Research Scientist, Google AI Taking a good group selfie can be tricky—you need to hover your finger above the shutter, keep everyone’s faces in the frame, look at the camera, make good expressions, try not to shake the camera and hope no one blinks when you finally press the shutter! After building the technology behind automatic photography with Google Clips , ...
Apr 08, 2019 Simulated Policy Learning in Video Models
Posted by Łukasz Kaiser and Dumitru Erhan, Research Scientists, Google AI Deep reinforcement learning (RL) techniques can be used to learn policies for complex tasks from visual inputs, and have been applied with great success to classic Atari 2600 games . Recent work in this field has shown that it is possible to get super-human performance in many of them, even in challenging exploration regimes such as that exhibited by Montezuma's Revenge ...