New York

We highlight the use of graphs (mathematical models for how items relate to each other) for understanding the differences and similarities between complex datasets.

Google Research launches a NLU-powered tool that provides answers and evidence to complex scientific questions about COVID-19.

We share the results of two papers highlighting innovations in the area of graph representation learning: the first paper introduces a novel technique to learn multiple embeddings per node and the second addresses the fundamental problem of hyperparameter tuning in graph embeddings.

Google Research launches AdaNet, an easy-to-use, TensorFlow-based framework for automatically learning high-quality models with minimal expert intervention.

Announcing BigQuery ML, a capability inside BigQuery that allows data scientists and analysts to build and deploy machine learning models on massive structured or semi-structured datasets.

This post presents the distributed algorithm we developed which is more applicable to large instances.

We developed a new efficient allocation algorithm for this problem, load balancing.

AudioSet provides a common, realistic-scale evaluation task for audio event detection and a starting point for a comprehensive vocabulary of sound events, designed to advance research into audio event detection and recognition.

We are releasing a major upgrade to SyntaxNet. This upgrade incorporates nearly a year’s worth of our research on multilingual language understanding, and is available to anyone interested in building systems for processing and understanding text.