公开(公告)号：US12198047B2

公开(公告)日：2025-01-14

申请号：US17122894

申请日：2020-12-15

Applicant: Salesforce.com, inc.

Inventor： James Bradbury ,  Stephen Joseph Merity ,  Caiming Xiong ,  Richard Socher

IPC: G06N3/08 ,  G06F17/16 ,  G06F40/00 ,  G06F40/216 ,  G06F40/30 ,  G06F40/44 ,  G06N3/04 ,  G06N3/044 ,  G06N3/045 ,  G06N3/10 ,  G10L15/16 ,  G10L15/18 ,  G10L25/30

Abstract: The technology disclosed provides a quasi-recurrent neural network (QRNN) encoder-decoder model that alternates convolutional layers, which apply in parallel across timesteps, and minimalist recurrent pooling layers that apply in parallel across feature dimensions.

公开(公告)号：US11080595B2

公开(公告)日：2021-08-03

申请号：US15420801

申请日：2017-01-31

Applicant: salesforce.com, inc.

Inventor： James Bradbury ,  Stephen Joseph Merity ,  Caiming Xiong ,  Richard Socher

IPC: G06N3/04 ,  G06N3/08 ,  G06F40/30 ,  G06F40/44 ,  G06F40/216 ,  G06F17/16 ,  G06N3/10 ,  G10L15/16 ,  G10L15/18 ,  G10L25/30 ,  G06F40/00

Abstract: The technology disclosed provides a quasi-recurrent neural network (QRNN) encoder-decoder model that alternates convolutional layers, which apply in parallel across timesteps, and minimalist recurrent pooling layers that apply in parallel across feature dimensions.

公开(公告)号：US11580359B2

公开(公告)日：2023-02-14

申请号：US16664508

申请日：2019-10-25

Applicant: salesforce.com, inc.

Inventor： Stephen Joseph Merity ,  Caiming Xiong ,  James Bradbury ,  Richard Socher

IPC: G06N3/04 ,  G06N3/084 ,  G06F40/284 ,  G06N3/08 ,  G06N7/00

Abstract: The technology disclosed provides a so-called “pointer sentinel mixture architecture” for neural network sequence models that has the ability to either reproduce a token from a recent context or produce a token from a predefined vocabulary. In one implementation, a pointer sentinel-LSTM architecture achieves state of the art language modeling performance of 70.9 perplexity on the Penn Treebank dataset, while using far fewer parameters than a standard softmax LSTM.

公开(公告)号：US20200065651A1

公开(公告)日：2020-02-27

申请号：US16664508

申请日：2019-10-25

Applicant: salesforce.com, inc.

Inventor： Stephen Joseph Merity ,  Caiming Xiong ,  James Bradbury ,  Richard Socher

IPC: G06N3/04 ,  G06N7/00 ,  G06N3/08 ,  G06F17/27

Abstract: The technology disclosed provides a so-called “pointer sentinel mixture architecture” for neural network sequence models that has the ability to either reproduce a token from a recent context or produce a token from a predefined vocabulary. In one implementation, a pointer sentinel-LSTM architecture achieves state of the art language modeling performance of 70.9 perplexity on the Penn Treebank dataset, while using far fewer parameters than a standard softmax LSTM.

公开(公告)号：US10565493B2

公开(公告)日：2020-02-18

申请号：US15421016

申请日：2017-01-31

Applicant: salesforce.com, inc.

Inventor： Stephen Joseph Merity ,  Caiming Xiong ,  James Bradbury ,  Richard Socher

IPC: G06N3/04 ,  G06F17/27 ,  G06N3/08 ,  G06N7/00

Abstract: The technology disclosed provides a so-called “pointer sentinel mixture architecture” for neural network sequence models that has the ability to either reproduce a token from a recent context or produce a token from a predefined vocabulary. In one implementation, a pointer sentinel-LSTM architecture achieves state of the art language modeling performance of 70.9 perplexity on the Penn Treebank dataset, while using far fewer parameters than a standard softmax LSTM.

公开(公告)号：US20180336453A1

公开(公告)日：2018-11-22

申请号：US15953265

申请日：2018-04-13

Applicant: salesforce.com, inc.

Inventor： Stephen Joseph Merity ,  Richard Socher ,  James Bradbury ,  Caiming Xiong

IPC: G06N3/04 ,  G06N3/08

Abstract: A system automatically generates recurrent neural network (RNN) architectures for performing specific tasks, for example, machine translation. The system represents RNN architectures using a domain specific language (DSL). The system generates candidate RNN architectures. The system predicts performances of the generated candidate RNN architectures, for example, using a neural network. The system filters the candidate RNN architectures based on their predicted performance. The system generates code for selected a candidate architectures. The generated code represents an RNN that is configured to perform the specific task. The system executes the generated code, for example, to evaluate an RNN or to use the RNN in an application.