Invention Application
- Patent Title: QUASI-RECURRENT NEURAL NETWORK BASED ENCODER-DECODER MODEL
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Application No.: US17122894Application Date: 2020-12-15
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Publication No.: US20210103816A1Publication Date: 2021-04-08
- Inventor: James BRADBURY , Stephen Joseph MERITY , Caiming XIONG , Richard SOCHER
- Applicant: salesforce.com, inc.
- Applicant Address: US CA San Francisco
- Assignee: salesforce.com, inc.
- Current Assignee: salesforce.com, inc.
- Current Assignee Address: US CA San Francisco
- Main IPC: G06N3/08
- IPC: G06N3/08 ; G06N3/04 ; G06F40/30 ; G06F40/44 ; G06F40/216 ; G06F17/16 ; G06N3/10

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.
Public/Granted literature
- US12198047B2 Quasi-recurrent neural network based encoder-decoder model Public/Granted day:2025-01-14
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