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Rethinking Attention with Performers

Posted by Krzysztof Choromanski and Lucy Colwell, Research Scientists, Google Research Transformer models have achieved state-of-the-art results across a diverse range of domains, including natural language, conversation, images, and even music. The core block of every Transformer architecture is the attention module, which computes similarity scores...

Announcing the Recipients of the 2020 Award for Inclusion Research

Posted by Negar Saei, Program Manager, Google Research At Google, it is our ongoing goal to support faculty who are conducting innovative research that will have positive societal impact. As part of that goal, earlier this year we launched the Award for Inclusion Research program, a global program that supports academic research in computing and technology...

Recreating Historical Streetscapes Using Deep Learning and Crowdsourcing

Posted by Raimondas Kiveris, Software Engineer, Google Research For many, gazing at an old photo of a city can evoke feelings of both nostalgia and wonder — what was it like to walk through Manhattan in the 1940s? How much has the street one grew up on changed? While Google Street View allows people to see what an area looks like in the present day,...

Measuring Gendered Correlations in Pre-trained NLP Models

Posted by Kellie Webster, Software Engineer, Google Research Natural language processing (NLP) has seen significant progress over the past several years, with pre-trained models like BERT, ALBERT, ELECTRA, and XLNet achieving remarkable accuracy across a variety of tasks. In pre-training, representations are learned from a large text corpus, e.g., Wikipedia,...

Announcing the 2020 Google PhD Fellows

Posted by Susie Kim, Program Manager, University RelationsGoogle created the PhD Fellowship Program in 2009 to recognize and support outstanding graduate students who seek to influence the future of technology by pursuing exceptional research in computer science and related fields. Now in its twelfth year, these Fellowships have helped support approximately...

Massively Large-Scale Distributed Reinforcement Learning with Menger

Posted by Amir Yazdanbakhsh, Research Scientist, and Junchaeo Chen, Software Engineer, Google Research In the last decade, reinforcement learning (RL) has become one of the most promising research areas in machine learning and has demonstrated great potential for solving sophisticated real-world problems, such as chip placement and resource management,...

Developing Real-Time, Automatic Sign Language Detection for Video Conferencing

Posted by Amit Moryossef, Research Intern, Google Research Video conferencing should be accessible to everyone, including users who communicate using sign language. However, since most video conference applications transition window focus to those who speak aloud, it makes it difficult for signers to “get the floor” so they can communicate easily and...

Audiovisual Speech Enhancement in YouTube Stories

Posted by Inbar Mosseri, Software Engineer and Michael Rubinstein, Research Scientist, Google Research While tremendous efforts are invested in improving the quality of videos taken with smartphone cameras, the quality of audio in videos is often overlooked. For example, the speech of a subject in a video where there are multiple people speaking or...

Advancing Instance-Level Recognition Research

Posted by Cam Askew and André Araujo, Software Engineers, Google Research Instance-level recognition (ILR) is the computer vision task of recognizing a specific instance of an object, rather than simply the category to which it belongs. For example, instead of labeling an image as “post-impressionist painting”, we’re interested in instance-level labels...

Advancing NLP with Efficient Projection-Based Model Architectures

Posted by Prabhu Kaliamoorthi, Software Engineer, Google Research Deep neural networks have radically transformed natural language processing (NLP) in the last decade, primarily through their application in data centers using specialized hardware. However, issues such as preserving user privacy, eliminating network latency, enabling offline functionality,...

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