Ge E Yang

20220245444, Aug 4, 2022

Jan 29, 2021

17/163299

- Redwood MA, US
Ritchie Zhao - Bellevue WA, US
Eric Chung - Woodinville WA, US
Douglas Burger - Bellevue WA, US
Bita Darvish Rouhani - Bellevue WA, US
Ge Yang - Bellevue WA, US
Nicolo Fusi - Watertown MA, US

G06N 3/08
G06F 17/18

Embodiments of the present disclosure include a system for optimizing an artificial neural network by configuring a model, based on a plurality of training parameters, to execute a training process, monitoring a plurality of statistics produced upon execution of the training process, and adjusting one or more of the training parameters, based on one or more of the statistics, to maintain at least one of the statistics within a predetermined range. In some embodiments, artificial intelligence (AI) processors may execute a training process on a model, the training process having an associated set of training parameters. Execution of the training process may produce a plurality of statistics. Control processor(s) coupled to the AI processor(s) may receive the statistics, and in accordance therewith, adjust one or more of the training parameters to maintain at least one of the statistics within a predetermined range during execution of the training process.

20190347548, Nov 14, 2019

May 10, 2018

15/976514

- Redmond WA, US
Ge Yang - Bellevue WA, US
Nicolo Fusi - Watertown MA, US
Francesco Paolo Casale - Boston MA, US

G06N 3/08

Systems and methods for selecting a neural network for a machine learning problem are disclosed. A method includes accessing an input matrix. The method includes accessing a machine learning problem space associated with a machine learning problem and multiple untrained candidate neural networks for solving the machine learning problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the machine learning problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the machine learning problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the machine learning problem. The method includes providing an output representing the selected at least one candidate neural network.