Browsing Colleges and Schools by Author "Qian, Xiaoning"
Now showing items 1-20 of 89
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Liao, Huiling (2022-07-28)Bayesian optimization (BO), a sequential design strategy for global optimization problem, has gained popularity during last decades for its capability of handling computationally expensive derivative-free objective functions ...
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Narayana, Sushirdeep (2017-05-08)Affect is the psychological display of emotion often described with three principal dimensions: 1) valence 2) arousal and 3) dominance. This thesis work explores the ability of computers to recognize human emotions using ...
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Das, Karimi Abhishek (2020-06-18)Medical diagnosis is the most critical component in the treatment of a patient. But diagnosis often is a complicated process since a myriad of diseases share the same symptoms. If a patient is diagnosed with a disease in ...
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Liu, Xueting (2019-11-21)Estimating density maps and counting the number of objects of interest from images has a wide range of applications, such as crowd counting, traffic monitoring, cell microscopy in biomedical imaging, plant counting in ...
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Shen, Jiayi (2022-12-08)Deep neural networks (DNNs) are resource-intensive and call for efficient compression methods to reduce the resource cost. For a composite DNN with various modules, the optimal resource allocation among these modules remains ...
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Guo, Zhengyu (2017-11-02)High-throughput sequencing has become one of the most powerful tools for studies in genomics, transcriptomics, epigenomics, and metagenomics. In recent years, HTS protocols for enhancing the understanding of the diverse ...
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Larsen, Allyson Elaine (2020-05-21)Markov chain Monte Carlo (MCMC) sampling methods often do not scale well to large datasets, so there has been an increased interest in approximate Markov chain Monte Carlo (aMCMC) sampling methods. We propose two different ...
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Wang, Yucheng (2021-04-27)Object counting in images has been studied extensively, in particular using deep network models recently. The existing counting models typically output the point estimates of the object counts in given images. However, ...
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Xiang, Ziyu (2020-11-17)Automatic Feature Engineering (AFE) aims to extract useful knowledge for interpretable predictions given data for the machine learning tasks of interest. Here, we develop AFE to extract dependency relationships that can ...
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Venugopal, Praveen Kumar (2020-11-23)Outlier Detection has been a fertile area of Machine Learning research for nearly two decades, leading to an explosive growth in the number of algorithms. Each algorithm is unique in its approach and with such a wide pool ...
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Dey, Sheelabhadra (2019-04-24)Brain imaging is an indispensable tool in neuroscience research. In many cases, research in this field involves capturing images of thin slices of animal brains using powerful high-resolution microscopes. The brain consists ...
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Fan, David Dawei (2015-04-17)The Theta neuron model is a spiking neuron model which, unlike traditional Leaky-Integrate-and-Fire neurons, can model spike latencies, threshold adaptation, bistability of resting and tonic firing states, and more. Previous ...
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Zamani Dadaneh, Siamak (2019-09-16)We develop a Bayesian framework for the analysis of high-throughput sequencing count data under a variety of settings, removing sophisticated ad-hoc pre-processing steps commonly required in existing algorithms. Specifically, ...
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Hajiramezanali, Mohammad Ehsan (2021-01-06)We propose a suite of Bayesian learning methods to address challenges arising from task and data heterogeneity in life science applications. First, we develop a novel multi-domain negative binomial (NB) factorization ...
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Jia, Xiaoqian (2019-11-18)Cone-beam computed tomography (CBCT) is increasingly used in radiotherapy for patient alignment and adaptive therapy where organ segmentation and target delineation are often required. However, due to the poor image quality, ...
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Nallaparaju, Venkata Vikas Varma (2018-11-21)RNA sequence analysis and structure prediction are classical topics of computational biology and a powerful tool to examine complex genomic data. Over the decades, various tools have been developed to predict RNA secondary ...
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Gadiyaram, Manasa (2022-04-19)Long non-coding RNA’s(lncRNA’s) are a type of RNA transcripts with a length of more than 200 nucleotides which cannot be translated into proteins. The study of lncRNAs is extremely important since it has been discovered ...
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Pakbin, Arash (2020-11-13)Alpha-beta network is a mixture of deep neural networks, implementing a mixture of experts, where each component is a neural network. It is trained using the expectation-maximization algorithm. It enables context-awareness ...
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Wilson, Nathan Robert (2022-08-17)The advancements in materials have driven significant progress in humanity which is largely enabled by a deeper understanding of fundamental materials science. For materials, the potential energy surface of the atomistic ...
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Cheng, Cheng (2020-04-10)3-Dimensional Convolutional Neural Networks (3D ConvNets) have been adopted for videobased action recognition task recently. Many 3D ConvNets, such as C3D, I3D, and Res3D, have been proposed and achieved great success. The ...