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Learning to Route by Task for Efficient Inference

Posted by Sneha Kudugunta, Research Software Engineer and Orhan Firat, Research Scientist, Google Research Scaling large language models has resulted in significant quality improvements natural language understanding (T5), generation (GPT-3) and multilingual neural machine translation (M4). One common approach to building a larger model is to increase...

Scaling Vision with Sparse Mixture of Experts

Posted by Carlos Riquelme, Research Scientist and Joan Puigcerver, Software Engineer, Google Research, Brain Team Advances in deep learning over the last few decades have been driven by a few key elements. With a small number of simple but flexible mechanisms (i.e., inductive biases such as convolutions or sequence attention), increasingly large datasets,...

Google Research: Themes from 2021 and Beyond

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. Early approaches, which often fell short, eventually gave rise to modern approaches that have been very successful....

A Scalable Approach for Partially Local Federated Learning

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 global model for all users, even though the users may differ in their data distributions. For...

Training Machine Learning Models More Efficiently with Dataset Distillation

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 made challenging due to the costs associated with training on such large datasets,...

Interpretable Deep Learning for Time Series Forecasting

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, and forecasting the future can unlock great value....

A Fast WordPiece Tokenization System

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 or word segments) in order to turn an unstructured input...

More Efficient In-Context Learning with GLaM

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 with very few or no training examples. While these models can perform better...

General and Scalable Parallelization for Neural Networks

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 in many real-world machine learning applications, such as computer vision, language understanding...

Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize

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 images pixel-by-pixel, the Vision...

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