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Articles 1 - 19 of 19
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Deep Collective Inference, John A. Moore
Deep Collective Inference, John A. Moore
Open Access Theses
Collective inference is widely used to improve classification in network datasets. However, despite recent advances in deep learning and the successes of recurrent neural networks (RNNs), researchers have only just recently begun to study how to apply RNNs to heterogeneous graph and network datasets. There has been recent work on using RNNs for unsupervised learning in networks (e.g., graph clustering, node embedding) and for prediction (e.g., link prediction, graph classification), but there has been little work on using RNNs for node-based relational classification tasks. In this paper, we provide an end-to-end learning framework using RNNs for collective inference. Our main …
Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm
Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm
Computer Science ETDs
Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in …
Towards A Deep Learning-Based Activity Discovery System, Eoin Rogers, John D. Kelleher, Robert J. Ross
Towards A Deep Learning-Based Activity Discovery System, Eoin Rogers, John D. Kelleher, Robert J. Ross
Conference papers
Activity discovery is a challenging machine learning problem where we seek to uncover new or altered behavioural patterns in sensor data. In this paper we motivate and introduce a novel approach to activity discovery based on modern deep learning techniques. We hypothesise that our proposed approach can deal with interleaved datasets in a more intelligent manner than most existing AD methods. We also build upon prior work building hierarchies of activities that capture the inherent ag- gregate nature of complex activities and show how this could plausibly be adapted to work with the deep learning technique we present. Finally, we …
When A Friend Online Is More Than A Friend In Life: Intimate Relationship Prediction In Microblogs, Yunshi Lan, Mengqi Zhang, Feida Zhu, Jing Jiang, Ee-Peng Lim
When A Friend Online Is More Than A Friend In Life: Intimate Relationship Prediction In Microblogs, Yunshi Lan, Mengqi Zhang, Feida Zhu, Jing Jiang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Microblogging services such as Twitter and Sina Weibo have been an important, if not indespensible, platform for people around the world to connect to one another. The rich content and user interactions on these platforms reveal insightful information about each user that are valuable for various real-life applications. In particular, user offline relationships, especially those intimate ones such as family members and couples, offer distinctive value for many business and social settings. In this study, we focus on using Sina Weibo to discover intimate offline relationships among users. The problem is uniquely interesting and challenging due to the difficulty in …
Conditional Computation In Deep And Recurrent Neural Networks, Andrew Scott Davis
Conditional Computation In Deep And Recurrent Neural Networks, Andrew Scott Davis
Doctoral Dissertations
Recently, deep learning models such as convolutional and recurrent neural networks have displaced state-of-the-art techniques in a variety of application domains. While the computationally heavy process of training is usually conducted on powerful graphics processing units (GPUs) distributed in large computing clusters, the resulting models can still be somewhat heavy, making deployment in resource- constrained environments potentially problematic. In this work, we build upon the idea of conditional computation, where the model is given the capability to learn how to avoid computing parts of the graph. This allows for models where the number of parameters (and in a sense, the …
Protein Residue-Residue Contact Prediction Using Stacked Denoising Autoencoders, Joseph Bailey Luttrell Iv
Protein Residue-Residue Contact Prediction Using Stacked Denoising Autoencoders, Joseph Bailey Luttrell Iv
Honors Theses
Protein residue-residue contact prediction is one of many areas of bioinformatics research that aims to assist researchers in the discovery of structural features of proteins. Predicting the existence of such structural features can provide a starting point for studying the tertiary structures of proteins. This has the potential to be useful in applications such as drug design where tertiary structure predictions may play an important role in approximating the interactions between drugs and their targets without expending the monetary resources necessary for preliminary experimentation. Here, four different methods involving deep learning, support vector machines (SVMs), and direct coupling analysis were …
Deep Semantic Image Interpolation, Joshua D. Little
Deep Semantic Image Interpolation, Joshua D. Little
McKelvey School of Engineering Theses & Dissertations
Image datasets often live on a continuum: Images from an outdoor scene vary from day to night, across different weather conditions, and over the course of seasons. Faces age and exhibit different expressions. We consider the problem of taking individual images from these datasets and explicitly manipulating those images to change where they lie on the continuum. We focus on a version of this problem that requires as little input as possible, and we build off of previous work using CNN features to construct an intermediate image manifold on which to manipulate the images. We also investigate a novel way …
Machine Learning Methods For Medical And Biological Image Computing, Rongjian Li
Machine Learning Methods For Medical And Biological Image Computing, Rongjian Li
Computer Science Theses & Dissertations
Medical and biological imaging technologies provide valuable visualization information of structure and function for an organ from the level of individual molecules to the whole object. Brain is the most complex organ in body, and it increasingly attracts intense research attentions with the rapid development of medical and bio-logical imaging technologies. A massive amount of high-dimensional brain imaging data being generated makes the design of computational methods for efficient analysis on those images highly demanded. The current study of computational methods using hand-crafted features does not scale with the increasing number of brain images, hindering the pace of scientific discoveries …
Machine Learning Methods For Brain Image Analysis, Ahmed Fakhry
Machine Learning Methods For Brain Image Analysis, Ahmed Fakhry
Computer Science Theses & Dissertations
Understanding how the brain functions and quantifying compound interactions between complex synaptic networks inside the brain remain some of the most challenging problems in neuroscience. Lack or abundance of data, shortage of manpower along with heterogeneity of data following from various species all served as an added complexity to the already perplexing problem. The ability to process vast amount of brain data need to be performed automatically, yet with an accuracy close to manual human-level performance. These automated methods essentially need to generalize well to be able to accommodate data from different species. Also, novel approaches and techniques are becoming …
A Computational Framework For Learning From Complex Data: Formulations, Algorithms, And Applications, Wenlu Zhang
A Computational Framework For Learning From Complex Data: Formulations, Algorithms, And Applications, Wenlu Zhang
Computer Science Theses & Dissertations
Many real-world processes are dynamically changing over time. As a consequence, the observed complex data generated by these processes also evolve smoothly. For example, in computational biology, the expression data matrices are evolving, since gene expression controls are deployed sequentially during development in many biological processes. Investigations into the spatial and temporal gene expression dynamics are essential for understanding the regulatory biology governing development. In this dissertation, I mainly focus on two types of complex data: genome-wide spatial gene expression patterns in the model organism fruit fly and Allen Brain Atlas mouse brain data. I provide a framework to explore …
Energy Consumption Prediction With Big Data: Balancing Prediction Accuracy And Computational Resources, Katarina Grolinger, Miriam Am Capretz, Luke Seewald
Energy Consumption Prediction With Big Data: Balancing Prediction Accuracy And Computational Resources, Katarina Grolinger, Miriam Am Capretz, Luke Seewald
Electrical and Computer Engineering Publications
In recent years, advances in sensor technologies and expansion of smart meters have resulted in massive growth of energy data sets. These Big Data have created new opportunities for energy prediction, but at the same time, they impose new challenges for traditional technologies. On the other hand, new approaches for handling and processing these Big Data have emerged, such as MapReduce, Spark, Storm, and Oxdata H2O. This paper explores how findings from machine learning with Big Data can benefit energy consumption prediction. An approach based on local learning with support vector regression (SVR) is presented. Although local learning itself is …
Using Perceptually Grounded Semantic Models To Autonomously Convey Meaning Through Visual Art, Derrall L. Heath
Using Perceptually Grounded Semantic Models To Autonomously Convey Meaning Through Visual Art, Derrall L. Heath
Theses and Dissertations
Developing advanced semantic models is important in building computational systems that can not only understand language but also convey ideas and concepts to others. Semantic models can allow a creative image-producing-agent to autonomously produce artifacts that communicate an intended meaning. This notion of communicating meaning through art is often considered a necessary part of eliciting an aesthetic experience in the viewer and can thus enhance the (perceived) creativity of the agent. Computational creativity, a subfield of artificial intelligence, deals with designing computational systems and algorithms that either automatically create original and functional products, or that augment the ability of humans …
Deepsense: A Gpu-Based Deep Convolutional Neural Network Framework On Commodity Mobile Devices, Huynh Nguyen Loc, Rajesh Krishna Balan, Youngki Lee
Deepsense: A Gpu-Based Deep Convolutional Neural Network Framework On Commodity Mobile Devices, Huynh Nguyen Loc, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
Recently, a branch of machine learning algorithms called deep learning gained huge attention to boost up accuracy of a variety of sensing applications. However, execution of deep learning algorithm such as convolutional neural network on mobile processor is non-trivial due to intensive computational requirements. In this paper, we present our early design of DeepSense - a mobile GPU-based deep convolutional neural network (CNN) framework. For its design, we first explored the differences between server-class and mobile-class GPUs, and studied effectiveness of various optimization strategies such as branch divergence elimination and memory vectorization. Our results show that DeepSense is able to …
Demo: Gpu-Based Image Recognition And Object Detection On Commodity Mobile Devices, Loc Nguyen Huynh, Rajesh Krishna Balan, Youngki Lee
Demo: Gpu-Based Image Recognition And Object Detection On Commodity Mobile Devices, Loc Nguyen Huynh, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
In this demo, we show that it is feasible to execute CNN for vision sensing tasks directly on mobile devices by leveraging integrated GPU. We propose our design of DeepSense framework based on OpenCL to execute deep learning algorithms in energy-efficient and fast manner.
Automatic Classification Of Perceived Gender From Face Images, Joseph Lemley, Sami Abdul-Wahid, Dipayan Banik
Automatic Classification Of Perceived Gender From Face Images, Joseph Lemley, Sami Abdul-Wahid, Dipayan Banik
Symposium Of University Research and Creative Expression (SOURCE)
Building software that can visually and accurately perceive gender from face images is an important step in making more intelligent machines. Several approaches to this problem have been suggested in the literature. We evaluate Histogram of Oriented Gradients, Dual Tree Complex Wavelet Transform (DTCWT) Principal Component Analysis (PCA) with Support Vector Machines (SVM) and compare them to Convolutional Neural Networks for this task. We train and test our classifiers with two benchmarks containing thousands of facial images. As expected, convolutional neural networks had the best performance while the performance of DTCWT varied most depending on the dataset used
Sparse Feature Learning For Image Analysis In Segmentation, Classification, And Disease Diagnosis., Ehsan Hosseini-Asl
Sparse Feature Learning For Image Analysis In Segmentation, Classification, And Disease Diagnosis., Ehsan Hosseini-Asl
Electronic Theses and Dissertations
The success of machine learning algorithms generally depends on intermediate data representation, called features that disentangle the hidden factors of variation in data. Moreover, machine learning models are required to be generalized, in order to reduce the specificity or bias toward the training dataset. Unsupervised feature learning is useful in taking advantage of large amount of unlabeled data, which is available to capture these variations. However, learned features are required to capture variational patterns in data space. In this dissertation, unsupervised feature learning with sparsity is investigated for sparse and local feature extraction with application to lung segmentation, interpretable deep …
Learning From Minimally Labeled Data With Accelerated Convolutional Neural Networks, Aysegul Dundar
Learning From Minimally Labeled Data With Accelerated Convolutional Neural Networks, Aysegul Dundar
Open Access Dissertations
The main objective of an Artificial Vision Algorithm is to design a mapping function that takes an image as an input and correctly classifies it into one of the user-determined categories. There are several important properties to be satisfied by the mapping function for visual understanding. First, the function should produce good representations of the visual world, which will be able to recognize images independently of pose, scale and illumination. Furthermore, the designed artificial vision system has to learn these representations by itself. Recent studies on Convolutional Neural Networks (ConvNets) produced promising advancements in visual understanding. These networks attain significant …
Big Data-Enabled Multiscale Serviceability Analysis For Aging Bridges, Yu Liang, Dalei Wu, Guirong Liu, Yaohang Li, Cuilan Gao, Zhongguo John Ma, Weidong Wu
Big Data-Enabled Multiscale Serviceability Analysis For Aging Bridges, Yu Liang, Dalei Wu, Guirong Liu, Yaohang Li, Cuilan Gao, Zhongguo John Ma, Weidong Wu
Computer Science Faculty Publications
This work is dedicated to constructing a multi-scale structural health monitoring system to monitor and evaluate the serviceability of bridges based on the Hadoop Ecosystem (MS-SHM-Hadoop). By taking the advantages of the fault-tolerant distributed file system called the Hadoop Distributed File System (HDFS) and high-performance parallel data processing engine called MapReduce programming paradigm, MS-SHM-Hadoop features include high scalability and robustness in data ingestion, fusion, processing, retrieval, and analytics. MS-SHM-Hadoop is a multi-scale reliability analysis framework, which ranges from nationwide bridge-surveys, global structural integrity analysis, and structural component reliability analysis. This Nationwide bridge survey uses deep-learning techniques to evaluate the bridge …
Probabilistic And Deep Learning Algorithms For The Analysis Of Imagery Data, Saikat Basu
Probabilistic And Deep Learning Algorithms For The Analysis Of Imagery Data, Saikat Basu
LSU Doctoral Dissertations
Accurate object classification is a challenging problem for various low to high resolution imagery data. This applies to both natural as well as synthetic image datasets. However, each object recognition dataset poses its own distinct set of domain-specific problems. In order to address these issues, we need to devise intelligent learning algorithms which require a deep understanding and careful analysis of the feature space. In this thesis, we introduce three new learning frameworks for the analysis of both airborne images (NAIP dataset) and handwritten digit datasets without and with noise (MNIST and n-MNIST respectively). First, we propose a probabilistic framework …