Jay P Parikh

20210012145, Jan 14, 2021

Jul 11, 2019

16/509062

- Reston VA, US
Sean PINKNEY - Reston VA, US
Prashanth VENKATRAMAN - Reston VA, US
Ashwath RAJENDRAN - Reston VA, US
Jay PARIKH - Reston VA, US

G06K 9/62
G06K 9/34
G06Q 30/02
G06N 3/08
G06F 17/21

Systems and methods for classifying images (e.g., ads) are described. An image is accessed. Optical character recognition is performed on at least a first portion of the image. Image recognition is performed via a convolutional neural network on at least a second portion of the image. At least one class for the image is automatically identified, via a fully connected neural network, based on one or more predictions, each of the one or more predictions being based on both the optical character recognition and the image recognition. Finally, the at least one class identified for the image is output.

20190114537, Apr 18, 2019

Oct 16, 2017

15/785074

- Menlo Park CA, US
Mohamed Fawzi Mokhtar Abd El Aziz - Kirkland WA, US
Aditya Rajkumar Kalro - Los Gatos CA, US
Hongzhong Jia - Cupertino CA, US
Jay Parikh - San Ramon CA, US

G06N 3/08
G06N 3/10

In one embodiment, a method includes establishing access to first and second different computing systems. A machine learning model is assigned for training to the first computing system, and the first computing system creates a check-point during training in response to a first predefined triggering event. The check-point may be a record of an execution state in the training of the machine learning model by the first computing system. In response to a second predefined triggering event, the training of the machine learning model on the first computing system is halted, and in response to a third predefined triggering event, the training of the machine learning model is transferred to the second computing system, which continues training the machine learning model starting from the execution state recorded by the check-point.