Michael D Potter

US Patent:

20220277195, Sep 1, 2022

Filed:

Jan 26, 2022

Appl. No.:

17/648996

Inventors:

- Waukesha WI, US
Michael Potter - Concord CA, US
Venkata Ratnam Saripalli - Danville CA, US

International Classification:

G06N 3/08

Abstract:

Techniques regarding autonomous data augmentation are provided. For example, one or more embodiments described herein can regard a system comprising a memory that can store computer-executable components. The system can also comprise a processor, operably coupled to the memory, that executes the computer-executable components stored in the memory. The computer-executable components can include a data augmentation component that executes a random unidimensional augmentation algorithm to augment a dataset for training a machine learning model via a plurality of augmentation operations. The random unidimensional augmentation algorithm can employ a global augmentation parameter that defines: a distortion magnitude associated with the plurality of augmentation operations, and a number of augmentation operations included in the plurality of augmentation operations.

US Patent:

20200327379, Oct 15, 2020

Filed:

Nov 30, 2019

Appl. No.:

16/699567

Inventors:

- Wauwatosa WI, US
Aritra Chowdhury - San Ramon CA, US
Junpyo Hong - San Ramon CA, US
Hsi-Ming Chang - San Ramon CA, US
Gopal B. Avinash - San Ramon CA, US
Venkata Ratnam Saripalli - San Ramon CA, US
Karley Yoder - Oakland CA, US
Michael Potter - Danville CA, US

International Classification:

G06K 9/62
G06N 3/08
G06N 3/04

Abstract:

An artificial intelligence platform and associated methods of training and use are disclosed. An example apparatus includes a data pipeline to: preprocess data using one or more preprocessing operations applied to features associated with the data; and enable debugging to visualize the preprocessed data. The example apparatus includes a network to: instantiate one or more differentiable operations in a training configuration to train an artificial intelligence model; capture feedback including optimization and loss information to adjust the training configuration; and store one or more metrics to evaluate performance of the artificial intelligence model. The example apparatus includes an estimator to: store the training configuration for the artificial intelligence model; configure the pipeline and the network based on the training configuration; iteratively link the pipeline and the network based on the training configuration; and initiate training of the artificial intelligence model using the linked pipeline and network.