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Filtering by: Keyword Deep Learning Subject Materials Science

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Utilizing Literature to Characterize Materials from Images

Description:

The past decade has seen the rapid progress of deep learning, which becomes a game-changing technique in different data-intensive domains, with the availability of large scale data, cost-effective computing hardware and more advanced learning theory and algorithms. Despite of the rapid progress of deep learning methods in daily-life applications, such...

Keyword:

Computational Photography, Representation Learning, Dataset Construction, Data Science, Computer Vision, and Deep Learning

Subject:

Materials Science, Information technology, and Computer science

Creator:

Jiang, Weixin

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

06/22/2022

Date Created:

2022-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_899812 and http://dissertations.umi.com/northwestern:16024
Fisher Score-Based Concept Drift Monitoring and Diagnosis with Applications to Microstructure Nonstationarity Analysis and Deep Learning Segmentation for Multiphase Materials

Description:

Supervised learning model is one of the most fundamental machine learning models. It can provide powerful capability of prediction by learning complex patterns hidden in many, sometimes thousands, predictors. It can also be used as a building block of other machine learning tasks, like unsupervised learning and reinforcement learning. Such...

Keyword:

Stochastic Microstructures, Fisher Score, Deep Learning, Machine Learning, Concept Drift, and Nonstationarity Analysis

Subject:

Materials Science, Statistics, and Computer science

Creator:

Zhang, Kungang

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

02/01/2021

Date Created:

2020-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_765218 and http://dissertations.umi.com/northwestern:15297

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Utilizing Literature to Characterize Materials from Images

Fisher Score-Based Concept Drift Monitoring and Diagnosis with Applications to Microstructure Nonstationarity Analysis and Deep Learning Segmentation for Multiphase Materials

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