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Latent Cross: Making Use of Context in Recurrent Recommender Systems
Latent Cross: Making Use of Context in Recurrent Recommender Systems Alex Beutel, Paul Covington, Sagar Jain, Can Xu, Jia Li∗, Vince Gatto, Ed H. Chi Google, Inc. Mountain View, California {alexbeutel, pcovington, sagarj, canxu, vgatto, edchi}@google.com, vena900620@gmail.com ABSTRACT The success of recommender systems often depends on their ability to understand and make use of the context of the
AudioSet
A sound vocabulary and datasetAudioSet consists of an expanding ontology of 632 audio event classes and a collection of 2,084,320 human-labeled 10-second sound clips drawn from YouTube videos. The ontology is specified as a hierarchical graph of event categories, covering a wide range of human and animal sounds, musical instruments and genres, and common everyday environmental sounds. By releasing
YouTube-8M: A Large and Diverse Labeled Video Dataset for Video Understanding Research
Sep 4th, 2019: Released the MediaPipe YouTube-8M feature extractor which extracts both visual and audio features. Jun 27th, 2019: Released the YouTube-8M Segments dataset. May 14th, 2018: Released an update to the dataset, with improved quality machine-generated labels, and reduced size / higher-quality video dataset. (YouTube-8M 2018). The YouTube-8M Segments dataset is an extension of the YouTub
Microsoft Word - brawny vs wimpy.docx
Brawny cores still beat wimpy cores, most of the time Urs Hölzle Google Slower but energy efficient “wimpy” cores only win for general workloads if their single-core speed is reasonably close to that of mid-range “brawny” cores. At Google, we’ve been long-term proponents of multicore architectures and throughput-oriented computing. In warehouse-scale systems1 throughput is more important than sing
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Publications – Google Research
Dremel: Interactive Analysis of Web-Scale Datasets