Dennis Wei, 42Sunnyvale, CA
Dennis Wei Phones & Addresses
Sunnyvale, CA
White Plains, NY
Tarrytown, NY
Dallas, TX
Ann Arbor, MI
Cambridge, MA
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Yehua Dennis Wei
Yehua Dennis Wei (Simplified Chinese: , born in Zhejiang, China in 1963) is a Chinese-American geographer. He is a professor in the Department of ...
Us Patents
Video Stabilization And Reduction Of Rolling Shutter Distortion
US Patent:
2011017, Jul 21, 2011
Filed:
Mar 3, 2010
Appl. No.:
12/716276
Inventors:
Wei Hong - Richardson TX, US
Dennis Wei - Cambridge MA, US
Aziz Umit Batur - Dallas TX, US
Dennis Wei - Cambridge MA, US
Aziz Umit Batur - Dallas TX, US
International Classification:
H04N 5/228
US Classification:
3482084, 348E05031
Abstract:
A method of processing a digital video sequence is provided that includes estimating compensated motion parameters and compensated distortion parameters (compensated M/D parameters) of a compensated motion/distortion (M/D) affine transformation for a block of pixels in the digital video sequence, and applying the compensated M/D affine transformation to the block of pixels using the estimated compensated M/D parameters to generate an output block of pixels, wherein translational and rotational jitter in the block of pixels is stabilized in the output block of pixels and distortion due to skew, horizontal scaling, vertical scaling, and wobble in the block of pixels is reduced in the output block of pixels.
Post-Hoc Local Explanations Of Black Box Similarity Models
US Patent:
2022039, Dec 8, 2022
Filed:
Jul 21, 2021
Appl. No.:
17/382310
Inventors:
- Armonk NY, US
- Troy NY, US
Dennis Wei - Sunnyvale CA, US
Amit Dhurandhar - Yorktown Heights NY, US
- Troy NY, US
Dennis Wei - Sunnyvale CA, US
Amit Dhurandhar - Yorktown Heights NY, US
International Classification:
G06K 9/62
G06N 20/00
G06N 20/00
Abstract:
Define a similarity measure between first and second points in a data space by operation of a machine learning model. Generate interpretable representations of the first and second points. Generate an interpretable local description of the similarity measure by approximating the similarity measure as a distance between the interpretable representations of the first and second points. The distance between the interpretable representations incorporates a matrix. Learn values for the matrix through optimizing a loss function evaluated on perturbations of the first and second points. Explain a value of the similarity measure between the first and second points using elements of the matrix. Assess the explanation of the value of the similarity measure using a rubric. In response to the assessment of the explanation of the value of the similarity measure, modify the machine learning model. Deploy the modified machine learning model.
Moving Decision Boundaries In Machine Learning Models
US Patent:
2022035, Nov 10, 2022
Filed:
May 5, 2021
Appl. No.:
17/308310
Inventors:
- Armonk NY, US
Elizabeth Daly - Dublin, IE
Rahul Nair - Dublin, IE
Massimiliano Mattetti - Dublin, IE
Dennis Wei - Sunnyvale CA, US
Karthikeyan Natesan Ramamurthy - Pleasantville NY, US
Elizabeth Daly - Dublin, IE
Rahul Nair - Dublin, IE
Massimiliano Mattetti - Dublin, IE
Dennis Wei - Sunnyvale CA, US
Karthikeyan Natesan Ramamurthy - Pleasantville NY, US
International Classification:
G06N 20/00
G06N 5/04
G06N 5/04
Abstract:
Embodiments are disclosed for a method. The method includes receiving feedback decision rules for multiple predictions by a trained machine learning model. generating a feedback rule set based on the feedback decision rules. The method further includes generating an updated training dataset based on an original training dataset and an updated feedback rule set. The updated feedback rule set resolves one or more conflicts of the feedback rule set, and the updated training dataset is configured to train the machine learning model to move a decision boundary. Generating the updated training dataset includes generating multiple updated training instances by applying one of the feedback decision rules to a training instance of the original training dataset.
Interpretable Model Changes
US Patent:
2022029, Sep 15, 2022
Filed:
Mar 10, 2021
Appl. No.:
17/197535
Inventors:
- Armonk NY, US
Rahul Nair - Dublin, IE
Oznur Alkan - Dublin, IE
Massimiliano Mattetti - Dublin, IE
Dennis Wei - Sunnyvale CA, US
Yunfeng Zhang - Chappaqua NY, US
Rahul Nair - Dublin, IE
Oznur Alkan - Dublin, IE
Massimiliano Mattetti - Dublin, IE
Dennis Wei - Sunnyvale CA, US
Yunfeng Zhang - Chappaqua NY, US
International Classification:
G06N 20/00
G06N 5/02
G06N 5/02
Abstract:
In a method for interpreting output of a machine learning model, a processor receives a first interpretable rule set. A processor may also receive a second interpretable rule set generated from a dataset and model-predicted labels classifying the dataset. A processor may also generate a difference metric and mapping between the first interpretable rule set and the second interpretable rule set.
Decision-Making Under Selective Labels
US Patent:
2023003, Feb 2, 2023
Filed:
Jul 20, 2021
Appl. No.:
17/381141
Inventors:
- Armonk NY, US
Dennis Wei - Sunnyvale CA, US
Dennis Wei - Sunnyvale CA, US
International Classification:
G16H 50/20
G16H 20/13
G06N 5/04
G06N 20/00
G16H 20/13
G06N 5/04
G06N 20/00
Abstract:
A computer-implemented method of decision-making using selective labels, includes receiving a conditional success probability value of a feature associated with an entity. A confidence value of the received success probability value is received. A parameter value that is a trade-off between a short-term learning and a long-term utility is selected. A decision is rendered to accept or reject the feature associated with the entity according to a machine learning policy.
Conditionally Independent Data Generation For Training Machine Learning Systems
US Patent:
2023002, 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
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
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.
Enhancing Fairness In Transfer Learning For Machine Learning Models With Missing Protected Attributes In Source Or Target Domains
US Patent:
2021015, May 27, 2021
Filed:
Nov 22, 2019
Appl. No.:
16/692974
Inventors:
- Armonk NY, US
Amanda Coston - Pittsburgh PA, US
Dennis Wei - Sunnyvale CA, US
Kush Raj Varshney - Ossining NY, US
Skyler Speakman - Nairobi, KE
Zairah Mustahsan - White Plains NY, US
Supriyo Chakraborty - White Plains NY, US
Amanda Coston - Pittsburgh PA, US
Dennis Wei - Sunnyvale CA, US
Kush Raj Varshney - Ossining NY, US
Skyler Speakman - Nairobi, KE
Zairah Mustahsan - White Plains NY, US
Supriyo Chakraborty - White Plains NY, US
International Classification:
G06N 20/00
Abstract:
A method of utilizing a computing device to correct source data used in machine learning includes receiving, by the computing device, first data. The computing device corrects the source data via an application of a covariate shift to the source data based upon the first data where the covariate shift re-weighs the source data.
Health Insurance Cost Prediction Reporting Via Private Transfer Learning
US Patent:
2019033, 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
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
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.
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