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Patrick Po Shun Ng, Age 771778 W 7Th St, Brooklyn, NY 11223

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Known as:
Patrick Po Ng Patrick P Ng Patrick Shun Ng Patrick Ml Ng Po Shun Ng Po Shung Ng Po S Ng Shun Ng Paul Ng
Related to:
Luen Shan Ng 72

Patrick Ng Phones & Addresses

Brooklyn, NY

Oakland Gardens, NY

Jersey City, NJ

New York, NY

Livingston, NJ

Work

Company: Avis budget group, inc. Address: 6 Sylvan Way, Parsippany, NJ 07054 Phones: (973) 496-4700 Position: Information technology fins Industries: Passenger Car Rental

Mentions for Patrick Po Shun Ng

Patrick Ng resumes & CV records

Resumes

Patrick Ng Photo 50

Patrick Ng - Flushing, NY

Work:
Queensborough Community College/CUNY Oct 2014 to 2000
Port of Entry Program(POE) Assistant BOON CHURCH OF OCM Jul 2012 to 2000
Summer Camp Teacher ASIAN AMERICANS FOR EQUALITIES - New York, NY Aug 2011 to Jun 2012
Intern College Advisor Assistant Queensborough Community College/CUNY - Bayside, NY Aug 2009 to Dec 2010
CUNYFirst Student Marketing Team Member Queensborough Community College/CUNY - Bayside, NY Apr 2008 to Dec 2010
Student Mentor / Instructor / Tech Support
Education:
Baruch College - New York, NY Jan 2014
Bachelor of Arts in Political Science Queensborough Community College - Bayside, NY May 2010
Associates in Applied Science (AAS) in Computer Information Systems
Patrick Ng Photo 51

Patrick Ng - Flushing, NY

Work:
Asian Americans for Equality Aug 2011 to 2000
Intern, Flushing High School Queensborough Community College - Bayside, NY Aug 2009 to Nov 2010
CUNYFirst Student Marketing Team Queensborough Community College - Bayside, NY Jun 2009 to Aug 2010
ePorfolio Instructor/Tutor/Tech Support Data Device Corporation - Bohemia, NY Jul 2008 to Aug 2009
Assembly Test Technician
Education:
Baruch College - New York, NY 2009 to 2014
Bachelor of Arts in Public Affairs Queensborough Community College - Bayside, NY 2007 to 2009
Associate in Applied Science in Computer Information Systems

Publications & IP owners

Us Patents

Multiple Stage Filtering For Natural Language Query Processing Pipelines

US Patent:
2023007, Mar 16, 2023
Filed:
Nov 14, 2022
Appl. No.:
18/055384
Inventors:
- Seattle WA, US
Zhiguo Wang - Syosset NY, US
Sharanabasappa Parashuram Revadigar - Bronxville NY, US
Ramesh M Nallapati - New Canaan CT, US
Bing Xiang - Mount Kisco NY, US
Stephen Michael Ash - Seattle WA, US
Timothy Jones - Brier WA, US
Sudipta Sengupta - Sammamish WA, US
Rishav Chakravarti - White Plains NY, US
Patrick Ng - Rego Park NY, US
Jiarong Jiang - Scarsdale NY, US
Hanbo Li - Seattle WA, US
Donald Harold Rivers Weidner - New York NY, US
Assignee:
Amazon Technologies, Inc. - Seattle WA
International Classification:
G06F 16/2452
G06F 40/295
G06N 20/00
G06F 16/242
Abstract:
Multiple stage filtering may be implemented for natural language query processing pipelines. Natural language queries may be received at a natural language query processing system and processed through a query language processing pipeline. The query language processing pipeline may filter candidate linkages for a natural language query before performing further filtering of the candidate linkages in the natural language query processing pipeline as part of generating an intermediate representation used to execute the natural language query.

Systems, Apparatuses, And Methods To Generate Synthetic Queries From Customer Data For Training Of Document Querying Machine Learning Models

US Patent:
2021015, May 27, 2021
Filed:
Nov 27, 2019
Appl. No.:
16/698080
Inventors:
- Seattle WA, US
Xiaofei MA - New York NY, US
Peng XU - Sunnyvale CA, US
Ramesh M. NALLAPATI - New Canaan CT, US
Bing XIANG - Mount Kisco NY, US
Sudipta SENGUPTA - Sammamish WA, US
Zhiguo WANG - Great Neck NY, US
Patrick NG - Jersey City NJ, US
International Classification:
G06F 16/9032
G06N 20/00
G06F 16/9038
G06K 9/62
Abstract:
Techniques for generation of synthetic queries from customer data for training of document querying machine learning (ML) models as a service are described. A service may receive one or more documents from a user, generate a set of question and answer pairs from the one or more documents from the user using a machine learning model trained to predict a question from an answer, and store the set of question and answer pairs generated from the one or more documents from the user. The question and answer pairs may be used to train another machine learning model, for example, a document ranking model, a passage ranking model, a question/answer model, or a frequently asked question (FAQ) model.

Possible Relatives

Cynthia Y Ng  David D Ng  Deborah H Ng  Dennis Ng  Denny J Ng  Dave J Ng  Dong K Ng  Patrick D Standley  Patrick O Johnston 

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