Index Catalog // Arch : Northwestern University Institutional Repository

Description:: In this work, we explore the utility of the three main types of neural networks: feed forward, convolutional, and recurrent. While using these networks, we develop a new way to model multiagent trajectory data, explore the use of multiple activation functions for neurons at each layer of a neural network,...
Keyword:: Stock Prediction, Deep Learning, Basketball Trajectory, Activation Functions, and Machine Learning
Subject:: Engineering Sciences and Applied Mathematics
Creator:: Mark Harmon
Owner:: Scholarly Digital Publishing
Date Uploaded:: 10/14/2019
Date Modified:: 10/14/2019
Date Created:: 2018-01-01
Resource Type:: Dissertation

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

Description:: Deep neural networks have achieved remarkable success in the past decade on tasks that were out of reach prior to the era of deep learning. Amongst the myriad reasons for these successes are powerful computational resources, large datasets, new optimization algorithms, and modern architecture designs. Most of the reasons are...
Keyword:: Statistical Physics, Deep Learning, and Machine Learning
Subject:: Statistical physics
Creator:: Wei, Mingwei
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/01/2021
Date Created:: 2020-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_777448 and http://dissertations.umi.com/northwestern:15367

Description:: Mammalian transcriptional regulation is well-known to be complex and highly context dependent. Different genetic and epigenetic features, including single nucleotide polymorphisms (SNPs) that function as cis- or trans-expression quantitative trait loci (eQTLs), transcription factor (TF) interaction profile with cis-regulatory elements (CREs), methylation of CpG dinucleotide sequences, and histone modification that...
Keyword:: Bioinformatics, Transcriptomics, Gene regulation, Deep Learning, Machine Learning, and Drug discovery
Subject:: Bioinformatics, Genetics, and Oncology
Creator:: Ji, Yanrong
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 06/28/2021
Date Created:: 2021-01-01
Resource Type:: Dissertation
Alternate Identifier:: etdadmin_upload_818497 and http://dissertations.umi.com/northwestern:15562

Description:: Recently, machine learning and deep learning, which have made many theoretical and empir- ical breakthroughs and is widely applied in various fields, attract a great number of researchers and practitioners. They have become one of the most popular research directions and plays a sig- nificant role in many fields, such...
Keyword:: Algorithm Design, Deep Learning, Optimization, and Machine Learning
Subject:: Applied mathematics and Operations research
Creator:: Fang, Biyi
Owner:: Scholarly Digital Publishing
Language:: en
Date Uploaded:: 02/02/2022
Date Created:: 2021-01-01
Resource Type:: Dissertation
Alternate Identifier:: http://dissertations.umi.com/northwestern:15817 and etdadmin_upload_855647

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