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Jan 17, 2018 Introducing the CVPR 2018 Learned Image Compression Challenge
Posted by Michele Covell, Research Scientist, Google Research Edit 17/01/2018: Due to popular request, the CLIC competition submission deadline has been extended to April 22. Please see compression.cc for more details. Image compression is critical to digital photography — without it, a 12 megapixel image would take 36 megabytes of storage, making most websites prohibitively large.
Jan 16, 2018 The Google Brain Team — Looking Back on 2017 (Part 1 of 2)
Posted by Jeff Dean, Google Senior Fellow, on behalf of the entire Google Brain Team The Google Brain team works to advance the state of the art in artificial intelligence by research and systems engineering, as one part of the overall Google AI effort. Last year we shared a summary of our work in 2016.
Jan 12, 2018 The Google Brain Team — Looking Back on 2017 (Part 2 of 2)
Posted by Jeff Dean, Google Senior Fellow, on behalf of the entire Google Brain Team The Google Brain team works to advance the state of the art in artificial intelligence by research and systems engineering, as one part of the overall Google AI effort. In Part 1 of this blog post , we shared some of our work in 2017 related to our broader research, from designing new machine learning algorithms and ...
Jan 04, 2018 TFGAN: A Lightweight Library for Generative Adversarial Networks
Posted by Joel Shor, Senior Software Engineer, Machine Perception (Crossposted on the Google Open Source Blog ) Training a neural network usually involves defining a loss function, which tells the network how close or far it is from its objective. For example, image classification networks are often given a loss function that penalizes them for giving wrong classifications; a network that mislabels a dog picture as a cat will get a high ...
Dec 21, 2017 Evaluation of Speech for the Google Assistant
Posted by Enrique Alfonseca, Staff Research Scientist, Google Assistant Voice interactions with technology are becoming a key part of our lives — from asking your phone for traffic conditions to work to using a smart device at home to turn on the lights or play music.
Dec 19, 2017 Tacotron 2: Generating Human-like Speech from Text
Posted by Jonathan Shen and Ruoming Pang, Software Engineers, on behalf of the Google Brain and Machine Perception Teams Generating very natural sounding speech from text (text-to-speech, TTS) has been a research goal for decades. There has been great progress in TTS research over the last few years and many individual pieces of a complete TTS system have greatly improved.
Dec 18, 2017 Introducing NIMA: Neural Image Assessment
Posted by Hossein Talebi, Software Engineer and Peyman Milanfar Research Scientist, Machine Perception Quantification of image quality and aesthetics has been a long-standing problem in image processing and computer vision. While technical quality assessment deals with measuring pixel-level degradations such as noise, blur, compression artifacts, etc., aesthetic assessment captures semantic level characteristics associated with emotions and beauty in images.
Dec 18, 2017 Improving End-to-End Models For Speech Recognition
Posted by Tara N. Sainath, Research Scientist, Speech Team and Yonghui Wu, Software Engineer, Google Brain Team Traditional automatic speech recognition (ASR) systems, used for a variety of voice search applications at Google, are comprised of an acoustic model (AM), a pronunciation model (PM) and a language model (LM), all of which are independently trained, and often manually designed, on different datasets [1].
Dec 15, 2017 Google at NIPS 2017
Posted by Christian Howard, Editor-in-Chief, Research Communications This week, Long Beach, California hosts the 31 st annual Conference on Neural Information Processing Systems (NIPS 2017), a machine learning and computational neuroscience conference that includes invited talks, demonstrations and presentations of some of the latest in machine learning research.
Dec 13, 2017 A Summary of the First Conference on Robot Learning
Posted by Vincent Vanhoucke, Principal Scientist, Google Brain Team and Melanie Saldaña, Program Manager, University Relations Whether in the form of autonomous vehicles, home assistants or disaster rescue units, robotic systems of the future will need to be able to operate safely and effectively in human-centric environments.
Dec 11, 2017 Introducing Appsperiments: Exploring the Potentials of Mobile Photography
Posted by Alex Kauffmann, Interaction Researcher, Google Research Each of the world's approximately two billion smartphone owners is carrying a camera capable of capturing photos and video of a tonal richness and quality unimaginable even five years ago. Until recently, those cameras behaved mostly as optical sensors, capturing light and operating on the resulting image's pixels.
Dec 05, 2017 Introducing a New Foveation Pipeline for Virtual/Mixed Reality
Posted by Behnam Bastani, Software Engineer Manager and Eric Turner, Software Engineer, Daydream Virtual Reality (VR) and Mixed Reality (MR) offer a novel way to immerse people into new and compelling experiences, from gaming to professional training. However, current VR/MR technologies present a fundamental challenge: to present images at the extremely high resolution required for immersion places enormous demands on the rendering engine and transmission process.
Dec 04, 2017 DeepVariant: Highly Accurate Genomes With Deep Neural Networks
Posted by Mark DePristo and Ryan Poplin, Google Brain Team (Crossposted on the Google Open Source Blog ) Across many scientific disciplines, but in particular in the field of genomics, major breakthroughs have often resulted from new technologies. From Sanger sequencing , which made it possible to sequence the human genome, to the microarray technologies that enabled the first large-scale genome-wide experiments, new instruments and tools have allowed us to look ever ...
Nov 30, 2017 Understanding Bias in Peer Review
Posted by Andrew Tomkins, Director of Engineering and William D. Heavlin, Statistician, Google Research In the 1600’s, a series of practices came into being known collectively as the “scientific method.” These practices encoded verifiable experimentation as a path to establishing scientific fact. Scientific literature arose as a mechanism to validate and disseminate findings, and standards of scientific peer review developed as a means to control the quality of entrants into this literature.
Nov 28, 2017 Interpreting Deep Neural Networks with SVCCA
Posted by Maithra Raghu, Google Brain Team Deep Neural Networks (DNNs) have driven unprecedented advances in areas such as vision , language understanding and speech recognition . But these successes also bring new challenges. In particular, contrary to many previous machine learning methods, DNNs can be susceptible to adversarial examples in classification, catastrophic forgetting of tasks in reinforcement learning , and mode collapse in generative modelling .
Nov 22, 2017 Understanding Medical Conversations
Posted by Katherine Chou, Product Manager and Chung-Cheng Chiu, Software Engineer, Google Brain Team Good documentation helps create good clinical care by communicating a doctor's thinking, their concerns, and their plans to the rest of the team. Unfortunately, physicians routinely spend more time doing documentation than doing what they love most — caring for patients.
Nov 21, 2017 SLING: A Natural Language Frame Semantic Parser
Posted by Michael Ringgaard, Software Engineer and Rahul Gupta, Research Scientist Until recently, most practical natural language understanding (NLU) systems used a pipeline of analysis stages, from part-of-speech tagging and dependency parsing to steps that computed a semantic representation of the input text. While this facilitated easy modularization of different analysis stages, errors in earlier stages would have cascading effects in later stages and the final representation, and the intermediate stage outputs ...
Nov 14, 2017 On-Device Conversational Modeling with TensorFlow Lite
Posted by Sujith Ravi, Research Scientist, Google Expander Team Earlier this year, we launched Android Wear 2.0 which featured the first "on-device" machine learning technology for smart messaging. This enabled cloud-based technologies like Smart Reply, previously available in Gmail , Inbox and Allo , to be used directly within any application for the first time, including third-party messaging apps, without ever having to connect to the cloud.
Nov 13, 2017 Fused Video Stabilization on the Pixel 2 and Pixel 2 XL
Posted by Chia-Kai Liang, Senior Staff Software Engineer and Fuhao Shi, Android Camera Team One of the most important aspects of current smartphones is easily capturing and sharing videos. With the Pixel 2 and Pixel 2 XL smartphones, the videos you capture are smoother and clearer than ever before, thanks to our Fused Video Stabilization technique based on both optical image stabilization (OIS) and electronic image stabilization (EIS).
Nov 09, 2017 Seamless Google Street View Panoramas
Posted by Mike Krainin, Software Engineer and Ce Liu, Research Scientist, Machine Perception In 2007, we introduced Google Street View , enabling you to explore the world through panoramas of neighborhoods, landmarks, museums and more, right from your browser or mobile device. The creation of these panoramas is a complicated process, involving capturing images from a multi-camera rig called a rosette, and then using image blending techniques to carefully stitch them all ...