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Articles 721 - 750 of 890
Full-Text Articles in Physical Sciences and Mathematics
Corrn: Cooperative Reflection Removal Network, Renjie Wen, Boxin Shi, Haoliang Li, Ling-Yu Duan, Ah-Hwee Tan, Alex C. Kot
Corrn: Cooperative Reflection Removal Network, Renjie Wen, Boxin Shi, Haoliang Li, Ling-Yu Duan, Ah-Hwee Tan, Alex C. Kot
Research Collection School Of Computing and Information Systems
Removing the undesired reflections from images taken through the glass is of broad application to various computer vision tasks. Non-learning based methods utilize different handcrafted priors such as the separable sparse gradients caused by different levels of blurs, which often fail due to their limited description capability to the properties of real-world reflections. In this paper, we propose a network with the feature-sharing strategy to tackle this problem in a cooperative and unified framework, by integrating image context information and the multi-scale gradient information. To remove the strong reflections existed in some local regions, we propose a statistic loss by …
Model-Based Deep Autoencoders For Characterizing Discrete Data With Application To Genomic Data Analysis, Tian Tian
Dissertations
Deep learning techniques have achieved tremendous successes in a wide range of real applications in recent years. For dimension reduction, deep neural networks (DNNs) provide a natural choice to parameterize a non-linear transforming function that maps the original high dimensional data to a lower dimensional latent space. Autoencoder is a kind of DNNs used to learn efficient feature representation in an unsupervised manner. Deep autoencoder has been widely explored and applied to analysis of continuous data, while it is understudied for characterizing discrete data. This dissertation focuses on developing model-based deep autoencoders for modeling discrete data. A motivating example of …
Statistical Machine Learning Methods For Mining Spatial And Temporal Data, Fei Tan
Statistical Machine Learning Methods For Mining Spatial And Temporal Data, Fei Tan
Dissertations
Spatial and temporal dependencies are ubiquitous properties of data in numerous domains. The popularity of spatial and temporal data mining has thus grown with the increasing prevalence of massive data. The presence of spatial and temporal attributes not only provides complementary useful perspectives, but also poses new challenges to the representation and integration into the learning procedure. In this dissertation, the involved spatial and temporal dependencies are explored with three genres: sample-wise, feature-wise, and target-wise. A family of novel methodologies is developed accordingly for the dependency representation in respective scenarios.
First, dependencies among discrete, continuous and repeated observations are studied …
Deep Morphological Neural Networks, Yucong Shen
Deep Morphological Neural Networks, Yucong Shen
Theses
Mathematical morphology is a theory and technique applied to collect features like geometric and topological structures in digital images. Determining suitable morphological operations and structuring elements for a give purpose is a cumbersome and time-consuming task. In this paper, morphological neural networks are proposed to address this problem. Serving as a non-linear feature extracting layers in deep learning frameworks, the efficiency of the proposed morphological layer is confirmed analytically and empirically. With a known target, a single-filter morphological layer learns the structuring element correctly, and an adaptive layer can automatically select appropriate morphological operations. For high level applications, the proposed …
Incorporating Figure Captions And Descriptive Text Into Mesh Term Indexing: A Deep Learning Approach, Xindi Wang
Incorporating Figure Captions And Descriptive Text Into Mesh Term Indexing: A Deep Learning Approach, Xindi Wang
Electronic Thesis and Dissertation Repository
The exponential increase of available documents online makes document classification an important application in natural language processing. The goal of text classification is to automatically assign categories to documents. Traditional text classifiers depend on features, such as, vocabulary and user-specified information which mainly relies on prior knowledge. In contrast, deep learning automatically learns effective features from data instead of adopting human-designed features. In this thesis, we specifically focus on biomedical document classification. Beyond text information from abstract and title, we also consider image and table captions, as well as paragraphs associated with images and tables, which we demonstrate to be …
Music Mood Classification Using Convolutional Neural Networks, Revanth Akella
Music Mood Classification Using Convolutional Neural Networks, Revanth Akella
Master's Projects
Grouping music into moods is useful as music is migrating from to online streaming services as it can help in recommendations. To establish the connection between music and mood we develop an end-to-end, open source approach for mood classification using lyrics. We develop a pipeline for tag extraction, lyric extraction, and establishing classification models for classifying music into moods. We investigate techniques to classify music into moods using lyrics and audio features. Using various natural language processing methods with machine learning and deep learning we perform a comparative study across different classification and mood models. The results infer that features …
Using Computer Vision To Quantify Coral Reef Biodiversity, Niket Bhodia
Using Computer Vision To Quantify Coral Reef Biodiversity, Niket Bhodia
Master's Projects
The preservation of the world’s oceans is crucial to human survival on this planet, yet we know too little to begin to understand anthropogenic impacts on marine life. This is especially true for coral reefs, which are the most diverse marine habitat per unit area (if not overall) as well as the most sensitive. To address this gap in knowledge, simple field devices called autonomous reef monitoring structures (ARMS) have been developed, which provide standardized samples of life from these complex ecosystems. ARMS have now become successful to the point that the amount of data collected through them has outstripped …
Robust Lightweight Object Detection, Siddharth Kumar
Robust Lightweight Object Detection, Siddharth Kumar
Master's Projects
Object detection is a very challenging problem in computer vision and has been a prominent subject of research for nearly three decades. There has been a promising in- crease in the accuracy and performance of object detectors ever since deep convolutional networks (CNN) were introduced. CNNs can be trained on large datasets made of high resolution images without flattening them, thereby using the spatial information. Their superior learning ability also makes them ideal for image classification and object de- tection tasks. Unfortunately, this power comes at the big cost of compute and memory. For instance, the Faster R-CNN detector required …
Intelligent Log Analysis For Anomaly Detection, Steven Yen
Intelligent Log Analysis For Anomaly Detection, Steven Yen
Master's Projects
Computer logs are a rich source of information that can be analyzed to detect various issues. The large volumes of logs limit the effectiveness of manual approaches to log analysis. The earliest automated log analysis tools take a rule-based approach, which can only detect known issues with existing rules. On the other hand, anomaly detection approaches can detect new or unknown issues. This is achieved by looking for unusual behavior different from the norm, often utilizing machine learning (ML) or deep learning (DL) models. In this project, we evaluated various ML and DL techniques used for log anomaly detection. We …
Deep Learning For Image Spam Detection, Tazmina Sharmin
Deep Learning For Image Spam Detection, Tazmina Sharmin
Master's Projects
Spam can be defined as unsolicited bulk email. In an effort to evade text-based spam filters, spammers can embed their spam text in an image, which is referred to as image spam. In this research, we consider the problem of image spam detection, based on image analysis. We apply various machine learning and deep learning techniques to real-world image spam datasets, and to a challenge image spam-like dataset. We obtain results comparable to previous work for the real-world datasets, while our deep learning approach yields the best results to date for the challenge dataset.
Image Retrieval Using Image Captioning, Nivetha Vijayaraju
Image Retrieval Using Image Captioning, Nivetha Vijayaraju
Master's Projects
The rapid growth in the availability of the Internet and smartphones have resulted in the increase in usage of social media in recent years. This increased usage has thereby resulted in the exponential growth of digital images which are available. Therefore, image retrieval systems play a major role in fetching images relevant to the query provided by the users. These systems should also be able to handle the massive growth of data and take advantage of the emerging technologies, like deep learning and image captioning. This report aims at understanding the purpose of image retrieval and various research held in …
Chatbots With Personality Using Deep Learning, Susmit Gaikwad
Chatbots With Personality Using Deep Learning, Susmit Gaikwad
Master's Projects
Natural Language Processing (NLP) requires the computational modelling of the complex relationships of the syntax and semantics of a language. While traditional machine learning methods are used to solve NLP problems, they cannot imitate the human ability for language comprehension. With the growth in deep learning, these complexities within NLP are easier to model, and be used to build many computer applications. A particular example of this is a chatbot, where a human user has a conversation with a computer program, that generates responses based on the user’s input. In this project, we study the methods used in building chatbots, …
Optimizing E-Commerce Product Classification Using Transfer Learning, Rashmeet Kaur Khanuja
Optimizing E-Commerce Product Classification Using Transfer Learning, Rashmeet Kaur Khanuja
Master's Projects
The global e-commerce market is snowballing at a rate of 23% per year. In 2017, retail e-commerce users were 1.66 billion and sales worldwide amounted to 2.3 trillion US dollars, and e-retail revenues are projected to grow to 4.88 trillion USD in 2021. With the immense popularity that e-commerce has gained over past few years comes the responsibility to deliver relevant results to provide rich user experience. In order to do this, it is essential that the products on the ecommerce website be organized correctly into their respective categories. Misclassification of products leads to irrelevant results for users which not …
Stock Market Prediction Using Ensemble Of Graph Theory, Machine Learning And Deep Learning Models, Pratik Patil
Stock Market Prediction Using Ensemble Of Graph Theory, Machine Learning And Deep Learning Models, Pratik Patil
Master's Projects
Efficient Market Hypothesis (EMH) is the cornerstone of the modern financial theory and it states that it is impossible to predict the price of any stock using any trend, fundamental or technical analysis. Stock trading is one of the most important activities in the world of finance. Stock price prediction has been an age-old problem and many researchers from academia and business have tried to solve it using many techniques ranging from basic statistics to machine learning using relevant information such as news sentiment and historical prices. Even though some studies claim to get prediction accuracy higher than a random …
Machine Learning Versus Deep Learning For Malware Detection, Parth Jain
Machine Learning Versus Deep Learning For Malware Detection, Parth Jain
Master's Projects
It is often claimed that the primary advantage of deep learning is that such models can continue to learn as more data is available, provided that sufficient computing power is available for training. In contrast, for other forms of machine learning it is claimed that models ‘‘saturate,’’ in the sense that no additional learning can occur beyond some point, regardless of the amount of data or computing power available. In this research, we compare the accuracy of deep learning to other forms of machine learning for malware detection, as a function of the training dataset size. We experiment with a …
Selecting Maximally-Predictive Deep Features To Explain What Drives Fixations In Free-Viewing, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge
Selecting Maximally-Predictive Deep Features To Explain What Drives Fixations In Free-Viewing, Matthias Kümmerer, Thomas S.A. Wallis, Matthias Bethge
MODVIS Workshop
No abstract provided.
Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu
Deep Autoencoder Neural Networks For Short-Term Traffic Congestion Prediction Of Transportation Networks, Sen Zhang, Yong Yao, Jie Hu, Yong Zhao, Shaobo Li, Jianjun Hu
Faculty Publications
Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available …
Machine Intelligence For Advanced Medical Data Analysis: Manifold Learning Approach, Fereshteh S Bashiri
Machine Intelligence For Advanced Medical Data Analysis: Manifold Learning Approach, Fereshteh S Bashiri
Theses and Dissertations
In the current work, linear and non-linear manifold learning techniques, specifically Principle Component Analysis (PCA) and Laplacian Eigenmaps, are studied in detail. Their applications in medical image and shape analysis are investigated.
In the first contribution, a manifold learning-based multi-modal image registration technique is developed, which results in a unified intensity system through intensity transformation between the reference and sensed images. The transformation eliminates intensity variations in multi-modal medical scans and hence facilitates employing well-studied mono-modal registration techniques. The method can be used for registering multi-modal images with full and partial data.
Next, a manifold learning-based scale invariant global shape …
Deep Learning Applications In Medical Image And Shape Analysis, Jingtao Yang
Deep Learning Applications In Medical Image And Shape Analysis, Jingtao Yang
Theses and Dissertations
Deep learning is one of the most rapidly growing fields in computer and data science in the past few years. It has been widely used for feature extraction and recognition in various applications. The training process as a black-box utilizes deep neural networks, whose parameters are adjusted by minimizing the difference between the predicted feedback and labeled data (so-called training dataset). The trained model is then applied to unknown inputs to predict the results that mimic human's decision-making. This technology has found tremendous success in many fields involving data analysis such as images, shapes, texts, audio and video signals and …
Deep Learning Vs Markov Model In Music Generation, Jeffrey Cruz
Deep Learning Vs Markov Model In Music Generation, Jeffrey Cruz
Honors College Theses
Artificial intelligence is one of the fastest growing fields at the moment in Computer Science. This is mainly due to the recent advances in machine learning and deep learning algorithms. As a result of these advances, deep learning has been used extensively in applications related to computerized audio/music generation. The main body of this thesis is an experiment. This experiment was based on a similar experiment done by Mike Kayser of Stanford University in 2013 for his thesis “Generative Models of Music” where he used Hidden Markov Models and tested the quality/accuracy of the music he generated using a music …
Triplet Loss Network For Unsupervised Domain Adaptation, Imad Eddine Ibrahim Bekkouch, Youssef Youssry, Rustam Gafarov, Adil Khan, Asad Masood Khattak
Triplet Loss Network For Unsupervised Domain Adaptation, Imad Eddine Ibrahim Bekkouch, Youssef Youssry, Rustam Gafarov, Adil Khan, Asad Masood Khattak
All Works
© 2019 by the authors. Domain adaptation is a sub-field of transfer learning that aims at bridging the dissimilarity gap between different domains by transferring and re-using the knowledge obtained in the source domain to the target domain. Many methods have been proposed to resolve this problem, using techniques such as generative adversarial networks (GAN), but the complexity of such methods makes it hard to use them in different problems, as fine-tuning such networks is usually a time-consuming task. In this paper, we propose a method for unsupervised domain adaptation that is both simple and effective. Our model (referred to …
Deepjit: An End-To-End Deep Learning Framework For Just-In-Time Defect Prediction, Thong Hoang, Hoa Khanh Dam, Yasutaka Kamei, David Lo, Naoyasu Ubayashi
Deepjit: An End-To-End Deep Learning Framework For Just-In-Time Defect Prediction, Thong Hoang, Hoa Khanh Dam, Yasutaka Kamei, David Lo, Naoyasu Ubayashi
Research Collection School Of Computing and Information Systems
Software quality assurance efforts often focus on identifying defective code. To find likely defective code early, change-level defect prediction – aka. Just-In-Time (JIT) defect prediction – has been proposed. JIT defect prediction models identify likely defective changes and they are trained using machine learning techniques with the assumption that historical changes are similar to future ones. Most existing JIT defect prediction approaches make use of manually engineered features. Unlike those approaches, in this paper, we propose an end-to-end deep learning framework, named DeepJIT, that automatically extracts features from commit messages and code changes and use them to identify defects. Experiments …
Patchnet: A Tool For Deep Patch Classification, Thong Hoang, Julia Lawall, Richard J. Oentaryo, Yuan Tian, David Lo
Patchnet: A Tool For Deep Patch Classification, Thong Hoang, Julia Lawall, Richard J. Oentaryo, Yuan Tian, David Lo
Research Collection School Of Computing and Information Systems
This work proposes PatchNet, an automated tool based on hierarchical deep learning for classifying patches by extracting features from commit messages and code changes. PatchNet contains a deep hierarchical structure that mirrors the hierarchical and sequential structure of a code change, differentiating it from the existing deep learning models on source code. PatchNet provides several options allowing users to select parameters for the training process. The tool has been validated in the context of automatic identification of stable-relevant patches in the Linux kernel and is potentially applicable to automate other software engineering tasks that can be formulated as patch classification …
Deep Reinforcement Learning-Based Portfolio Management, Nitin Kanwar
Deep Reinforcement Learning-Based Portfolio Management, Nitin Kanwar
Computer Science and Engineering Theses
Machine Learning is at the forefront of every field today. The subfields of Machine Learning called Reinforcement Learning and Deep Learning, when combined have given rise to advanced algorithms which have been successful at reaching or surpassing the human-level performance at playing Atari games to defeating multiple times champion at Go. These successes of Machine Learning have attracted the interest of the financial community and have raised the question if these techniques could also be applied in detecting patterns in the financial markets. Until recently, mathematical formulations of dynamical systems in the context of Signal Processing and Control Theory have …
From Body To Brain: Using Artificial Intelligence To Identify User Skills & Intentions In Interactive Scenarios, Michalis Papakostas
From Body To Brain: Using Artificial Intelligence To Identify User Skills & Intentions In Interactive Scenarios, Michalis Papakostas
Computer Science and Engineering Dissertations
Artificial Intelligence has probably been the most rapidly evolving field of science during the last decade. Its numerous real-life applications have radically altered the way we experience daily-living with great impact in some of the most basic aspects of human lives including but not limited to health and well-being, communication and interaction, education, driving, daily, and entertainment. Human-Computer Interaction (HCI) is the field of Computer Science lying in the epicenter of this evolution and is responsible for transforming rudimentary research findings and theoretical principles into intuitive tools, responsible for enhancing human performance, increasing productivity and ensuring safety. Two of the …
Ultra-Context: Maximizing The Context For Better Image Caption Generation, Ankit Khare
Ultra-Context: Maximizing The Context For Better Image Caption Generation, Ankit Khare
Computer Science and Engineering Theses
Several combinations of visual and semantic attention have been geared towards developing better image captioning architectures. In this work we introduce a novel combination of word-level semantic context with image feature-level visual context, which provides a more holistic overall context for image caption generation. This approach does not require training any explicit network structure, using any external resource for training semantic attributes, or supervision during any training step. The proposed architecture addresses the significance of learning to find context at three levels to achieve a better trade-off as well as a balance between the two lines of attentiveness (word-level and …
Deep Learning Applications In Medical Image And Shape Analysis, Jingtao Yang
Deep Learning Applications In Medical Image And Shape Analysis, Jingtao Yang
Theses and Dissertations
Deep learning is one of the most rapidly growing fields in computer and data science in the past few years. It has been widely used for feature extraction and recognition in various applications. The training process as a black-box utilizes deep neural networks, whose parameters are adjusted by minimizing the difference between the predicted feedback and labeled data (so-called training dataset). The trained model is then applied to unknown inputs to predict the results that mimic human's decision-making. This technology has found tremendous success in many fields involving data analysis such as images, shapes, texts, audio and video signals and …
Patchnet: A Tool For Deep Patch Classification, Thong Hoang, Julia Lawall, Richard J. Oentaryo, Yuan Tian, David Lo
Patchnet: A Tool For Deep Patch Classification, Thong Hoang, Julia Lawall, Richard J. Oentaryo, Yuan Tian, David Lo
Research Collection School Of Computing and Information Systems
This work proposes PatchNet, an automated tool based on hierarchical deep learning for classifying patches by extracting features from commit messages and code changes. PatchNet contains a deep hierarchical structure that mirrors the hierarchical and sequential structure of a code change, differentiating it from the existing deep learning models on source code. PatchNet provides several options allowing users to selectparameters for the training process. The tool has been validated in the context of automatic identification of stable-relevant patches in the Linux kernel and is potentially applicable to automate other software engineering tasks that can be formulated as patch classification problems. …
Weighted Random Search For Hyperparameter Optimization, Adrian-Cǎtǎlin Florea, Rǎzvan Andonie
Weighted Random Search For Hyperparameter Optimization, Adrian-Cǎtǎlin Florea, Rǎzvan Andonie
All Faculty Scholarship for the College of the Sciences
We introduce an improved version of Random Search (RS), used here for hyperparameter optimization of machine learning algorithms. Unlike the standard RS, which generates for each trial new values for all hyperparameters, we generate new values for each hyperparameter with a probability of change. The intuition behind our approach is that a value that already triggered a good result is a good candidate for the next step, and should be tested in new combinations of hyperparameter values. Within the same computational budget, our method yields better results than the standard RS. Our theoretical results prove this statement. We test our …
Softmax And Mcfadden's Discrete Choice Under Interval (And Other) Uncertainty, Bartłomiej Jacek Kubica, Laxman Bokati, Olga Kosheleva, Vladik Kreinovich
Softmax And Mcfadden's Discrete Choice Under Interval (And Other) Uncertainty, Bartłomiej Jacek Kubica, Laxman Bokati, Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
One of the important steps in deep learning is softmax, when we select one of the alternatives with a probability depending on its expected gain. A similar formula describes human decision making: somewhat surprisingly, when presented with several choices with different expected equivalent monetary gain, we do not just select the alternative with the largest gain; instead, we make a random choice, with probability decreasing with the gain -- so that it is possible that we will select second highest and even third highest value. Both formulas assume that we know the exact value of the expected gain for each …