公开(公告)号：US20180189950A1

公开(公告)日：2018-07-05

申请号：US15859943

申请日：2018-01-02

Applicant: Google Inc.

Inventor： Mohammad Norouzi ,  Anelia Angelova ,  Michael Gygli

IPC: G06T7/10 ,  G06N5/04 ,  G06F15/18

CPC classification number: G06T7/10 ,  G06N3/0454 ,  G06N3/084 ,  G06N5/046 ,  G06N20/00 ,  G06T7/11 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A computer-implemented method includes receiving an input data item including a plurality of data elements, and generating a predicted structured output for the input data item. Generating the predicted structured output includes iteratively performing the following operations: receiving a current structured output that assigns, to each of the data elements, a respective current value for each of the one or more categories; processing the input data item and the current output using a value neural network, in which the value neural network has been trained to process the input data item and the current output to generate a value score that is an estimate of how accurately the current output predicts the likelihoods that the elements belong to the one or more categories; and updating the current structured output by adjusting the current values in the current output to increase the value score generated by the value neural network.

公开(公告)号：US10068557B1

公开(公告)日：2018-09-04

申请号：US15684537

申请日：2017-08-23

Applicant: Google Inc.

Inventor： Jesse Engel ,  Mohammad Norouzi ,  Karen Simonyan ,  Adam Roberts ,  Cinjon Resnick ,  Sander Etienne Lea Dieleman ,  Douglas Eck

IPC: G10H1/00 ,  G06N3/08 ,  G06N3/04 ,  G10H7/12

Abstract: The present disclosure provides systems and methods that include or otherwise leverage a machine-learned neural synthesizer model. Unlike a traditional synthesizer which generates audio from hand-designed components like oscillators and wavetables, the neural synthesizer model can use deep neural networks to generate sounds at the level of individual samples. Learning directly from data, the neural synthesizer model can provide intuitive control over timbre and dynamics and enable exploration of new sounds that would be difficult or impossible to produce with a hand-tuned synthesizer. As one example, the neural synthesizer model can be a neural synthesis autoencoder that includes an encoder model that learns embeddings descriptive of musical characteristics and an autoregressive decoder model that is conditioned on the embedding to autoregressively generate musical waveforms that have the musical characteristics one audio sample at a time.

公开(公告)号：US20150178383A1

公开(公告)日：2015-06-25

申请号：US14576907

申请日：2014-12-19

Applicant: Google Inc.

Inventor： Gregory Sean Corrado ,  Tomas Mikolov ,  Samy Bengio ,  Yoram Singer ,  Jonathon Shlens ,  Andrea L. Frome ,  Jeffrey Adgate Dean ,  Mohammad Norouzi

IPC: G06F17/30

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data objects. One of the methods includes obtaining data that associates each term in a vocabulary of terms with a respective high-dimensional representation of the term; obtaining classification data for a data object, wherein the classification data includes a respective score for each of a plurality of categories, and wherein each of the categories is associated with a respective category label; computing an aggregate high-dimensional representation for the data object from high-dimensional representations for the category labels associated with the categories and the respective scores; identifying a first term in the vocabulary of terms having a high-dimensional representation that is closest to the aggregate high-dimensional representation; and selecting the first term as a category label for the data object.

Abstract translation: 方法，系统和装置，包括在计算机存储介质上编码的计算机程序，用于对数据对象进行分类。 其中一种方法包括获得将术语词汇中的每个术语与该术语的相应高维表示相关联的数据; 获取数据对象的分类数据，其中分类数据包括多个类别中的每一个的相应分数，并且其中每个类别与相应的分类标签相关联; 从与类别和相应分数相关联的类别标签的高维表示中计算数据对象的聚合高维表示; 识别具有最接近聚合高维表示的高维表示的术语词汇表中的第一项; 并选择第一项作为数据对象的类别标签。