The Basque Association

of Language Industries

Document Actions

RSS feeds

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 ...
Apr 04, 2019 Capturing Special Video Moments with Google Photos
Posted by Sudheendra Vijayanarasimhan and David Ross, Software Engineers Recording video of memorable moments to share with friends and loved ones has become commonplace. But as anyone with a sizable video library can tell you, it's a time consuming task to go through all that raw footage searching for the perfect clips to relive or share with family and friends.
Apr 02, 2019 Using Deep Learning to Improve Usability on Mobile Devices
Posted by Yang Li, Research Scientist, Google AI Tapping is the most commonly used gesture on mobile interfaces, and is used to trigger all kinds of actions ranging from launching an app to entering text. While the style of clickable elements (e.g., buttons) in traditional desktop graphical user interfaces is often conventionally defined, on mobile interfaces it can still be difficult for people to distinguish tappable versus non-tappable elements due to the ...
Mar 29, 2019 Unifying Physics and Deep Learning with TossingBot
Posted by Andy Zeng, Student Researcher, Robotics at Google Though considerable progress has been made in enabling robots to grasp objects efficiently , visually self adapt or even learn from real-world experiences , robotic operations still require careful consideration in how they pick up, handle, and place various objects -- especially in unstructured settings.
Mar 20, 2019 Reducing the Need for Labeled Data in Generative Adversarial Networks
Posted by Mario Lučić, Research Scientist and Marvin Ritter, Software Engineer, Google AI Zürich Generative adversarial networks (GANs) are a powerful class of deep generative models.The main idea behind GANs is to train two neural networks: the generator, which learns how to synthesise data (such as an image), and the discriminator, which learns how to distinguish real data from the ones synthesised by the generator.
Mar 19, 2019 Measuring the Limits of Data Parallel Training for Neural Networks
Posted by Chris Shallue, Senior Software Engineer and George Dahl, Senior Research Scientist, Google AI Over the past decade, neural networks have achieved state-of-the-art results in a wide variety of prediction tasks, including image classification , machine translation , and speech recognition . These successes have been driven, at least in part, by hardware and software improvements that have significantly accelerated neural network training.
Mar 18, 2019 A Summary of the Google Flood Forecasting Meets Machine Learning Workshop
Posted by Sella Nevo, Senior Software Engineer and Rainier Aliment, Program Manager Recently, we hosted the Google Flood Forecasting Meets Machine Learning workshop in our Tel Aviv office, which brought hydrology and machine learning experts from Google and the broader research community to discuss existing efforts in this space, build a common vocabulary between these groups, and catalyze promising collaborations.
Mar 15, 2019 Google Faculty Research Awards 2018
Posted by Maggie Johnson, VP, Education and Negar Saei, Program Manager, University Relations We just completed another round of the Google Faculty Research Awards , our annual open call for proposals on computer science and related topics, such as quantum computing, machine learning, algorithms and theory, natural language processing and more.
Mar 14, 2019 Harnessing Organizational Knowledge for Machine Learning
Posted by Alex Ratner, Stanford University and Cassandra Xia, Google AI One of the biggest bottlenecks in developing machine learning (ML) applications is the need for the large, labeled datasets used to train modern ML models. Creating these datasets involves the investment of significant time and expense, requiring annotators with the right expertise.
Mar 13, 2019 An All-Neural On-Device Speech Recognizer
Posted by Johan Schalkwyk, Google Fellow, Speech Team In 2012, speech recognition research showed significant accuracy improvements with deep learning , leading to early adoption in products such as Google's Voice Search . It was the beginning of a revolution in the field: each year, new architectures were developed that further increased quality, from deep neural networks (DNNs) to recurrent neural networks (RNNs), long short-term memory networks (LSTMs), convolutional networks (CNNs), and ...