Yao Chia Yang

20220365443, Nov 17, 2022

Nov 15, 2019

17/767020

- Santa Clara CA, US
Yinfeng DONG - San Jose CA, US
Rick R. HUNG - Tainan, TW
Yao Cheng YANG - Mountain View CA, US
Tsaichuan KAO - San Ramon CA, US

G03F 7/20

A verification device for verifying a design file for digital lithography comprises a memory and a controller. The memory comprises the design file. The controller is configured to access the design file and apply one or more compliance rules to the design file to determine compliance of the design file. The compliance rules comprises at least one of detecting non-orthogonal edges within the design file, detecting non-compliant overlapping structures within the design file, and detecting a non-compliant interaction between a reference layer of the design file and a target layer of the design file. The controller is further configured to verify the design file in response to a comparison of a number of non-orthogonal edges, non-compliant overlapping structures and non-compliant interactions to a threshold.

20210224584, Jul 22, 2021

Jan 16, 2020

16/744506

- Dublin, IE
Yao A. Yang - San Francisco CA, US
Saeideh Shahrokh Esfahani - Mountain View CA, US
Andrew E. Fano - Lincolnshire IL, US
David William Vinson - San Francisco CA, US
Timothy M. Shea - Merced CA, US

G06K 9/62
G06N 5/00
G06N 20/10

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for clustering data are disclosed. In one aspect, a method includes the actions of receiving feature vectors. The actions further include accessing rules that each relate one or more values of the feature vectors to a respective label of a plurality of labels. The actions further include, based on the rules, generating heuristics that each identify related values of the feature vectors. The actions further include, for each of the heuristics, generating a matrix that reflects a similarity of the feature vectors. The actions further include, based on the matrices that each reflects a respective similarity of the feature vectors, generating clusters that each include a subset of the feature vectors. The actions further include, for each cluster, determining a label of the plurality of labels.

20210110302, Apr 15, 2021

Jan 22, 2020

16/749717

- Dublin, IE
Yao YANG - San Francisco CA, US
Teresa Sheausan TUNG - Tustin CA, US
Mohamad Mehdi NASR-AZADANI - Menlo Park CA, US
Zaid TASHMAN - San Francisco CA, US
Ruiwen LI - San Francisco CA, US

G06N 20/00
G06N 7/00
G06K 9/62
G06F 9/30

The present disclosure relates to a system, a method, and a product for optimizing hyper-parameters for generation and execution of a machine-learning model under constraints. The system includes a memory storing instructions and a processor in communication with the memory. When executed by the processor, the instructions cause the processor to obtain input data and an initial hyper-parameter set; for an iteration, to build a machine learning model based on the hyper-parameter set, evaluate the machine learning model based on the target data to obtain a performance metrics set, and determine whether the performance metrics set satisfies the stopping criteria set. If yes, the instructions cause the processor to perform an exploitation process to obtain an optimal hyper-parameter set, and exit the iteration; if no, perform an exploration process to obtain a next hyper-parameter set, and perform a next iteration with using the next hyper-parameter set as the hyper-parameter set.

20200401915, Dec 24, 2020

Jun 19, 2020

16/906759

- Dublin, IE
Mohamad Mehdi Nasr-Azadani - Menlo Park CA, US
Yao A. Yang - Sunnyvale CA, US
Zaid Tashman - San Francisco CA, US
Maziyar Baran Pouyan - Emeryville CA, US

Accenture Global Solutions Limited - Dublin

G06N 5/04
G06N 20/00

A systems implements a gradient descent calculation, regression calculation, or other machine learning calculation on a dataset (e.g., a global dataset) using a coordination node including coordination circuitry that coordinates multiple worker nodes to create a distributed calculation architecture. In some cases, the worker nodes each hold a portion of the dataset and operate on their respective portion. In some cases, the gradient descent calculation, regression calculation, or other machine learning calculation is used to implement a targeted maximum likelihood scheme for causal inference estimation. The targeted maximum likelihood scheme may be used to conduct causal analysis of the observational data.

20200387836, Dec 10, 2020

Jun 3, 2020

16/891980

- Dublin, IE
Matthew Kujawinski - San Jose CA, US
Andrew Nam - San Francisco CA, US
Yao Yang - San Francisco CA, US
Teresa Sheausan Tung - Tustin CA, US
Jurgen Albert Weichenberger - Woking, Surrey, GB

G06N 20/20

Complex computer system architectures are described for providing a machine learning model management tool that monitors, detects, and makes revisions to machine learning models to prevent declines and maintain robustness and fairness in machine learning model performance in production over time. The machine learning model management tool achieves its goals via intelligent management, organization, and orchestration of detection, inspection, and correction engines.