Dennis C Wei

US Patent:

8493262, Jul 23, 2013

Filed:

Feb 11, 2011

Appl. No.:

13/026085

Inventors:

Petros T. Boufounos - Boston MA, US
Dennis Wei - Cambridge MA, US

Assignee:

Mitsubishi Electric Research Laboratories, Inc. - Cambridge MA

International Classification:

G01S 13/00

US Classification:

342 25R

Abstract:

A saturated input signal acquired by a synthetic aperture radar (SAR) system is processed by estimating a reconstruction that generated the input signal, reproducing an input signal from an estimated reconstruction to generate a reproduced signal, comparing the reproduced signal with the input signal; adjusting an estimated reconstruction based on the comparison; and iterating from the reproducing step until a termination condition is reached.

US Patent:

20230021338, Jan 26, 2023

Filed:

Jul 7, 2021

Appl. No.:

17/368925

Inventors:

- Armonk NY, US
Prasanna Sattigeri - Acton MA, US
Karthikeyan Shanmugam - Elmsford NY, US
Dennis Wei - Sunnyvale CA, US
Murat Kocaoglu - West Lafayette IN, US
Karthikeyan Natesan Ramamurthy - Pleasantville NY, US

International Classification:

G06N 3/08
G06N 3/04

Abstract:

A method for training a machine learning system using conditionally independent training data includes receiving an input dataset (p(x, y, z)). A generative adversarial network, that includes a generator and a first discriminator, uses the input dataset to generate a training data (p(x, y, z)) by generating the values (x, y, z). The first discriminator determines a first loss (L) based on (x, y, z) and (x, y, z). A divergence calculator modifies the training data based on a dependence measure (γ). The divergence calculator includes a second discriminator and a third discriminator. Modifying the training data includes receiving a reference value ({tilde over (y)}), and computing, by the second discriminator, a second loss (L) based on (x, y, z) and (x, {tilde over (y)}, z). The third discriminator computes a third loss (L) based on (y, z) and ({tilde over (y)}, z). Further, a fourth loss (L) is computed based on Land L. The training data is output from the generator if Land Lsatisfy a predetermined condition.

US Patent:

20190333155, Oct 31, 2019

Filed:

Apr 27, 2018

Appl. No.:

15/964856

Inventors:

- Armonk NY, US
Emily A. Ray - Hastings on Hudson NY, US
Dennis Wei - White Plains NY, US
Gigi Y.C. Yuen-Reed - Tampa FL, US

International Classification:

G06Q 40/08
G06N 99/00

Abstract:

A method, computer system, and a computer program product for generating and reporting a plurality of health insurance cost predictions via private transfer learning is provided. The present invention may include retrieving a set of source data, and a set of target data. The present invention may then include creating and anonymizing a plurality of source data sets, and at least one target data set. The present invention may further include generating one or more source learner models, and a target learner model. The present invention may then include combining the one or more generated source learner models and the generated target learner model to generate a transfer learner. The present invention may further include generating a prediction based on the generated transfer learner.

US Patent:

20170140393, May 18, 2017

Filed:

Nov 13, 2015

Appl. No.:

14/940560

Inventors:

- Armonk NY, US
Aleksandra Mojsilovic - New York NY, US
Karthikeyan Natesan Ramamurthy - Yorktown Heights NY, US
Dennis Wei - White Plains NY, US
Gigi Y. Yuen-Reed - Tampa FL, US

International Classification:

G06Q 30/02
G06Q 40/08
G06F 17/30

Abstract:

A system, method and program product for cost attribution using multiple factors, in which transactional data sets from two or more time periods are analyzed based on multiple potential factors in the data sets that can be correlated to cost. The potential factors are systematically analyzed to identify a set of cost factors and compute the cost impact for each cost factor. An infrastructure is disclosed having a data selection system; a potential factors system; a factor hierarchy system; an actionability class system; a factor processing system and a cost factor reporting system for providing the cost impact of the set of cost factors based on analysis of the transactional data sets.