Category google-researches

Posted by Jeff Dean, Senior Fellow and SVP of Google Research, on behalf of the entire Google Research community Over the last several decades, I’ve witnessed a lot of change in the fields of machine learning (ML) and computer science.…

Posted by Karan Singhal, Senior Software Engineer, Google Research Federated learning enables users to train a model without sending raw data to a central server, thus avoiding the collection of privacy-sensitive data. Often this is done by learning a single…

Posted by Timothy Nguyen1, Research Engineer and Jaehoon Lee, Senior Research Scientist, Google Research For a machine learning (ML) algorithm to be effective, useful features must be extracted from (often) large amounts of training data. However, this process can be…

Posted by Sercan O. Arik, Research Scientist and Tomas Pfister, Engineering Manager, Google Cloud Multi-horizon forecasting, i.e. predicting variables-of-interest at multiple future time steps, is a crucial challenge in time series machine learning. Most real-world datasets have a time component,…

Posted by Xinying Song, Staff Software Engineer and Denny Zhou, Senior Staff Research Scientist, Google Research Tokenization is a fundamental pre-processing step for most natural language processing (NLP) applications. It involves splitting text into smaller units called tokens (e.g., words…

Posted by Andrew M Dai and Nan Du, Research Scientists, Google Research, Brain Team Large language models (e.g., GPT-3) have many significant capabilities, such as performing few-shot learning across a wide array of tasks, including reading comprehension and question answering…

Posted by Yuanzhong Xu and Yanping Huang, Software Engineers; Google Research, Brain Team Scaling neural networks, whether it be the amount of training data used, the model size or the computation being utilized, has been critical for improving model quality…

Posted by Michael Ryoo, Research Scientist, Robotics at Google and Anurag Arnab, Research Scientist, Google Research Transformer models consistently obtain state-of-the-art results in computer vision tasks, including object detection and video classification. In contrast to standard convolutional approaches that process…

Posted by Jason Wei, AI Resident and Dan Garrette, Research Scientist, Google Research In recent years, pre-trained language models, such as BERT and GPT-3, have seen widespread use in natural language processing (NLP). By training on large volumes of text,…