Hao Cheng

US Patent:

7996331, Aug 9, 2011

Filed:

Aug 31, 2007

Appl. No.:

11/848636

Inventors:

Rajendra Singh Solanki - Cary NC, US
Yinhua Wang - Cary NC, US
Jie Zhong - Raleigh NC, US
Hao Cheng - Athens GA, US

Assignee:

SAS Institute Inc. - Cary NC

International Classification:

G06Q 99/00

US Classification:

705400, 705 11, 705 731

Abstract:

Computer-implemented systems and methods for regular pricing optimization. A system can include decomposing a pricing situation into sub-problems. A non-linear optimization problem is solved to determine continuous optimal prices. A mixed integer linear programming problem is solved to snap prices to grid points.

US Patent:

20210326751, Oct 21, 2021

Filed:

May 22, 2020

Appl. No.:

16/882296

Inventors:

- Redmond WA, US
Hao Cheng - Bellevue WA, US
Yu Wang - Bellevue WA, US
Jianfeng Gao - Woodinville WA, US
Weizhu Chen - Kirkland WA, US
Pengcheng He - Sammamish WA, US
Hoifung Poon - Bellevue WA, US

Assignee:

Microsoft Technology Licensing, LLC - Redmond WA

International Classification:

G06N 20/00
G06N 3/08
G06F 40/284
G06K 9/62

Abstract:

This document relates to training of machine learning models. One example method involves providing a machine learning model having one or more mapping layers. The one or more mapping layers can include at least a first mapping layer configured to map components of pretraining examples into first representations in a space. The example method also includes performing a pretraining stage on the one or more mapping layers using the pretraining examples. The pretraining stage can include adding noise to the first representations of the components of the pretraining examples to obtain noise-adjusted first representations. The pretraining stage can also include performing a self-supervised learning process to pretrain the one or more mapping layers using at least the first representations of the training data items and the noise-adjusted first representations of the training data items.