Tian Zhou

US Patent:

20220198526, Jun 23, 2022

Filed:

Mar 9, 2022

Appl. No.:

17/690938

Inventors:

- New York NY, US
Tian Zhou - Sunnyvale CA, US
Aaron Eliasib Flores - Menlo Park CA, US

International Classification:

G06Q 30/02
G06N 20/00
G06N 3/08

Abstract:

One or more computing devices, systems, and/or methods are provided. A machine learning model may be trained using a plurality of sets of information. One or more pruning operations may be performed in association with the training to generate a machine learning model with sparse vector representations associated with features of the plurality of sets of information. A request for content associated with a client device may be received. A set of features associated with the request for content may be determined. A plurality of positive signal probabilities associated with a plurality of content items may be determined using the machine learning model based upon one or more sparse vector representations, of the machine learning model, associated with the set of features. A content item may be selected from the plurality of content items for presentation via the client device based upon the plurality of positive signal probabilities.

US Patent:

20200234112, Jul 23, 2020

Filed:

Apr 10, 2019

Appl. No.:

16/380813

Inventors:

- Santa Clara CA, US
Tian Zhou - Menlo Park CA, US
Qun Gu - San Jose CA, US

International Classification:

G06N 3/08
G06F 7/544

Abstract:

A method of adaptive quantization for a convolutional neural network, includes at least one of receiving an acceptable model accuracy, determining a float value multiply accumulate for the layer based on a float value weight and a float value input, quantizing the float value weight at multiple weight quantization precisions, quantizing the float value input at multiple input quantization precisions, determining a multiply accumulate at multiple multiply accumulate quantization precisions based on the weight quantization precisions and the input quantization precisions, determining multiple quantization errors based on differences between the float value multiply accumulate and the multiple multiply accumulate quantization precisions and selecting one of the multiple weight quantization precisions, one of the multiple input quantization precisions and one of the multiple multiply accumulate quantization precisions based on the predetermined acceptable model accuracy and the multiple quantization errors.