Li Wen Yang

US Patent:

20130197994, Aug 1, 2013

Filed:

Nov 5, 2012

Appl. No.:

13/668828

Inventors:

AOL Advertising Inc. - New York NY, US
Jianlong ZHANG - Sunnyvale CA, US
Robert Alden LUENBERGER - Los Altos CA, US
Scott Robert STRICKLAND - Redwood City CA, US
Yih-Shin Cindy HUANG - Cupertino CA, US
Seyed Mohammad ZIAEMOHSENI - East Palo Alto CA, US
Li YANG - Sunnyvale CA, US
Hans W. UHLIG - Hayward CA, US

Assignee:

AOL ADVERTISING INC. - New York NY

International Classification:

G06Q 30/02

US Classification:

705 1445, 705 1441

Abstract:

System and methods are provided for analyzing and displaying digital content and advertisements over electronic networks. In accordance with one implementation, a method is provided that calculates, using at least one processor, an engagement metric (including, for example, click probability) for a specific combination of time, promotion slot, and user segment, for an individual promotion of a set of promotions, and calculates a value of a landing page associated with the promotion. The method then can multiply the engagement metric and the value to determine the valuation of the promotion. The method can operate recursively by using previous calculations in later runs of the same method.

US Patent:

20230078473, Mar 16, 2023

Filed:

Sep 14, 2022

Appl. No.:

17/932104

Inventors:

Deliang Fan - Tempe AZ, US
Adnan Siraj Rakin - Tempe AZ, US
Li Yang - Tempe AZ, US
Chaitali Chakrabarti - Tempe AZ, US
Yu Cao - Gilbert AZ, US
Jae-sun Seo - Tempe AZ, US
Jingtao Li - Tempe AZ, US

Assignee:

Arizona Board of Regents on behalf of Arizona State University - Scottsdale AZ

International Classification:

G06N 3/08
G06N 3/04

Abstract:

A robust and accurate binary neural network, referred to as RA-BNN, is provided to simultaneously defend against adversarial noise injection and improve accuracy. Recently developed adversarial weight attack, a.k.a. bit-flip attack (BFA), has shown enormous success in compromising deep neural network (DNN) performance with an extremely small amount of model parameter perturbation. To defend against this threat, embodiments of RA-BNN adopt a complete binary neural network (BNN) to significantly improve DNN model robustness (defined as the number of bit-flips required to degrade the accuracy to as low as a random guess). To improve clean inference accuracy, a novel and efficient two-stage network growing method is proposed and referred to as early growth. Early growth selectively grows the channel size of each BNN layer based on channel-wise binary masks training with Gumbel-Sigmoid function. Apart from recovering the inference accuracy, the RA-BNN after growing also shows significantly higher resistance to BFA.

US Patent:

20200052338, Feb 13, 2020

Filed:

Oct 21, 2019

Appl. No.:

16/659005

Inventors:

- Detroit MI, US
- Oakland CA, US
Yunfeng LU - Los Angeles CA, US
Mei CAI - Bloomfield Hills MI, US
Fang DAI - Troy MI, US
Li YANG - Troy MI, US

Assignee:

GM GLOBAL TECHNOLOGY OPERATIONS LLC - Detroit MI
THE REGENTS OF THE UNIVERSITY OF CALIFORNIA - Oakland CA

International Classification:

H01M 10/0567
H01M 10/42
H01M 10/0568
H01M 10/0569

Abstract:

An example electrolyte includes a solvent, a lithium salt, and an additive selected from the group consisting of a mercaptosilane, a mercaptosiloxane, and combinations thereof. The electrolyte may be used in a method for making a solid electrolyte interface (SEI) layer on a surface of an electrode. A negative electrode structure may be formed from the method.

US Patent:

20160172710, Jun 16, 2016

Filed:

Dec 3, 2015

Appl. No.:

14/958842

Inventors:

- OAKLAND CA, US
- DETROIT MI, US
Yunfeng LU - Los Angeles CA, US
Mei Cai - Bloomfield Hills MI, US
Fang Dai - Troy MI, US
Li Yang - Troy MI, US

International Classification:

H01M 10/0567
H01M 10/0568
H01M 10/42
H01M 10/0569

Abstract:

An example electrolyte includes a solvent, a lithium salt, and an additive selected from the group consisting of a mercaptosilane, a mercaptosiloxane, and combinations thereof. The electrolyte may be used in a method for making a solid electrolyte interface (SEI) layer on a surface of an electrode. A negative electrode structure may be formed from the method.