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  • Real-time Speech Workload Estimation

    Creator:
    Fortune, Julian D.
    Abstract:
    Demanding task environments (e.g., supervising a remotely piloted aircraft) require performing tasks quickly and accurately; however, periods of low and high operator workload can decrease task performance. Intelligent modulation of the system’s demands and interaction modality in response to changes in operator workload state may increase performance by avoiding undesirable...
    Resource Type:
    Honors College Thesis
    Full Text:
    layer consisted of a single neuron, corresponding to the speech workload estimate. The ADAM optimizer

  • Sequential supervised learning and conditional random fields

    Creator:
    Ashenfelter, Adam J.
    Abstract:
    Supervised learning is concerned with discovering the relationship between example sets of features and their corresponding classes. The traditional supervised learning formulation assumes that all examples are independent from one another. The order of the examples contains no information. Nonetheless, many problems have a sequential nature. Classifiers for these problems...
    Resource Type:
    Masters Thesis
    Full Text:
    thesis.dvi c© Copyright by Adam J. Ashenfelter December 4, 2003

  • Learning cost-sensitive diagnostic policies from data

    Creator:
    Zubek, Valentina Bayer
    Abstract:
    In its simplest form, the process of diagnosis is a decision-making process in which the diagnostician performs a sequence of tests culminating in a diagnostic decision. For example, a physician might perform a series of simple measurements (body tem- perature, weight, etc.) and laboratory measurements (white blood count, CT scan,...
    Resource Type:
    Dissertation
    Full Text:
    , Chandra Reddy, Tom Amoth, Eric Chown, Tony Fountain, Wes Pin- chot, Dan Forrest, Adam Ashenfelter, Ashit

  • Imperative Programming with Variational Effects

    Creator:
    Grasley, Alexander B.
    Abstract:
    Variation is a commonly encountered element of modern software systems. Recent research into variation has led to increasing interest in the development of variational programming languages and corresponding variational models of execution. Variational imperative languages pose a particular challenge due to the complex interactions between side effects and variation. The...
    Resource Type:
    Masters Thesis
    Full Text:
    Erwig, Arash Termehchy, and Adam Branscum. In particular I’ve had the pleasure of being in many of

  • Layered rendering model for human teeth

    Creator:
    Shetty, Sudarshanram
    Abstract:
    This study introduces a layered model for rendering human teeth. Human teeth exhibit complex light interaction due to their layered structure. While the lighting responses of teeth have been studied in the dental industry for the production of realistic looking dentures, to our knowledge this is the first study of...
    Resource Type:
    Masters Thesis
    Full Text:
    Zhang, Dr Toshimi Minoura and Dr Adam Branscum for their time and inputs. TABLE OF CONTENTS Page 1

  • User interface design considerations for emerging input technologies in iTV

    Creator:
    Wolford, Justin
    Abstract:
    Streaming media and interactive television viewing experiences are becoming more commonplace with the introduction of services such as Netflix Streaming, the Apple TV, and Google TV aided by the increase adoption of broadband internet. As these services make their way into the living room, and developers struggle to accommodate more...
    Resource Type:
    Masters Thesis
    Full Text:
    and Adam Branscum for taking the time to read my thesis and to attend my defense. I would also

  • Gender and participation in early stages of the free/open source software joining process

    Creator:
    Gilbertson, Claire E.
    Abstract:
    Free/Open Source Software (FOSS) is a powerful development paradigm for creating software. Increasingly more FOSS projects, like Firefox and Android, are integrated into mainstream technology. It is important that FOSS projects serve its diverse user base well. Several surveys have found that existing FOSS communities are very homogenous populations and...
    Resource Type:
    Masters Thesis

  • Deep Learning Temporal Logic Specifications

    Creator:
    Hekkala, Claire M.
    Abstract:
    This thesis seeks to explain a new method of extracting temporal logic formulas from time series data using machine learning. Two strategies were followed during the development of this research: first, a generative adversarial network was combined with already-existing temporal logic extraction code by Belta. This was achieved by injecting...
    Resource Type:
    Honors College Thesis
    Full Text:
    () model.add(InternalFormula(input_shape=(2,))) model.add(Robust()) opt = Adam(lr = 0.0004

  • Counterfactual States for Atari Agents via Generative Deep Learning

    Creator:
    Olson, Matthew L.
    Abstract:
    Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past work has mainly focused on explaining why an action was chosen in a given state. A different type of explanation that is useful is...
    Resource Type:
    Masters Thesis
    Full Text:
    an Adam optimizer, with parameters α = 1−4, β1 = 0, β2 = .9. These networks were typically trained

  • Characterizing and Addressing Dataset Shift with Deep Learning

    Creator:
    Olson, Matthew L.
    Abstract:
    This dissertation delves into understanding, characterizing, and addressing dataset shift in deep learning, a pervasive issue for deployed machine learning systems. Integral aspects of the problem are examined: We start with the use of counterfactual explanations in order to characterize the behavior of deep reinforcement learning agents in visual input...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Matthew L

  • An editor for message passing programs

    Creator:
    Pai, Akash L.
    Abstract:
    Preparing code for parallelization is one of the most time consuming programming tasks; approximately 24% of the program development activity is spend in this phase. In message passing systems, this is usually achieved by the addition of run-time message-passing library calls. The primitives provided by different message-passing libraries are similar,...
    Resource Type:
    Capstone Project
    Full Text:
    ) An Editor for Message Passing Programs by Akash L

  • Machine Learning Methods for Computational Sustainability

    Creator:
    Liu, Liping
    Abstract:
    Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these ecosystems are facing more and more pressure from human activities. Machine learning can play an important role in promoting sustainability as a large amount of data is being collected from ecosystems. There are at least three important...
    Resource Type:
    Dissertation
    Full Text:
    different numbers of cells L . . . . . 54 5.1 TTK improves the SVM decision boundary (“d. b.”). Square

  • The Pursuit of Hoppiness : Propelling Hop into the Genomic Era

    Creator:
    Hill, Steven T.
    Abstract:
    Hop (Humulus lupulus L. var lupulus) is a plant of great cultural significance, used as a medicinal herb for thousands of years, and for flavor and as a preservative in brewing beer. Studies of the medicinal effects of the unique compounds produced by hop have led to interest from the...
    Resource Type:
    Masters Thesis
    Full Text:
    David A. Hendrix John A. Henning Hop (Humulus lupulus L. var

  • Action Segmentation with Limited Supervision

    Creator:
    Li, Jun
    Abstract:
    In this dissertation, we address action segmentation in videos under limited supervision. The goal of action segmentation is to predict an action class for each frame of a video. The limited supervision means ground truth labels of video frames are not available in training. We focus on three types of...
    Resource Type:
    Dissertation
    Full Text:
    initial MAP action sequence (c̃, l̃). c̃ may be missing some classes (e.g., “blue” label) from the ground

  • Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

    Creator:
    Khorram, Saeed
    Abstract:
    In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...
    Resource Type:
    Masters Thesis
    Full Text:
    test accuracy of 94.43% (10 layers), and 91.75% (20 layers) respectively, while SGD and Adam (10

  • Graph Imitation Learning for Optimal Joint Beamforming and Antenna Selection

    Creator:
    Shrestha, Sagar
    Abstract:
    Transmit beamforming is an important technique employed to improve efficiency and signal quality in wireless communication systems by steering signals towards their in- tended users. It often arises jointly with the antenna selection problem due to various reasons, such as limited number of radio frequency (RF) chains and energy/resource effi-...
    Resource Type:
    Masters Thesis
    Full Text:
    ,M,L) = (8, 4, 4). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 LIST OF TABLES

  • Exploration of regression models for cancer noncoding mutation recurrence

    Creator:
    Xu, Tanjin
    Abstract:
    An important impact of the genome technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Integrated with current well-studied massive knowledge and findings about the role of protein-coding mutations in cancer, demystifying the functional...
    Resource Type:
    Masters Thesis
    Full Text:
    ) Typically, likelihood maximization is employed to estimate α and β. The likelihood function is: L(α, β) = n

  • Task Grapher : task graph scheduling in multiprocessor computer systems

    Creator:
    Chu, Lee-Jung Julie
    Abstract:
    This report presents a graphical tool for implementation of the scheduling heuristics provided by Kruatrachue. The input of scheduling is a task graph and the output is a schedule in the form of a Gantt chart. The implemented scheduling heuristics are: 1) Hu's HLF (highest level first) algorithm. 2) Yu's...
    Resource Type:
    Capstone Project
    Full Text:
    11 Li J J u u References [1] Adam, T. L., Chandy, K. M., and Dickson, J. R., "A Comparison

  • Relational Networks for Visual Relationship Detection in Images

    Creator:
    Nguyen, Khoi
    Abstract:
    This thesis is about visual relationship detection. This is an important task in computer vision. The goal is to detect all visual relationships in a given image between objects. This thesis presents a new approach to this problem. Our approach does not use an object detector as a common pre-processing...
    Resource Type:
    Masters Thesis
    Full Text:
    v is u a l re la ti o n sh ip d et ec ti o n . It h a s fo u r m o d u le s: o b je

  • Analysis of Convolutional Neural Network Image Pre-processing Methods

    Creator:
    Jacobsen, Matthew
    Abstract:
    Oceanic plankton have both a large impact on our oceans health and atmo- spheric balance of Co2. The overall health of planktonic life is determined by many physical, chemical, and ecological factors, that drive taxonomic abundances and the relative amount of non-living biomass called detritus. Recent advances in microscopic imaging...
    Resource Type:
    Honors College Thesis
    Full Text:
    3.2 Model 11 3.2.1 AlexNet 11 3.2.2 Adam 12 3.2.3 Cross-entropy loss 13 3.2.4 Other hyper

  • Transcribing Solo Piano Performances from Audio to MIDI Using a Neural Network

    Creator:
    Ebert, Victoria
    Abstract:
    Automatic Music Transcription is a growing area of interest in Music Information Retrieval, and recent research has shown promise using onset detection on spectrograms. We propose a deep learning model that takes raw audio as input in order to transcribe a solo piano performance from audio to MIDI without complicated...
    Resource Type:
    Honors College Thesis
    Full Text:
    end our model with a layer of 131 nodes. After experimenting with both Adam and RMSProp optimizers

  • A Distributed Scheme for Accelerating and Scaling PSPNet

    Creator:
    Krishna, Ajay
    Abstract:
    Advances in deep learning based image processing have led to their adoption for a wide range of applications, and in tow with these developments is a dramatic increase in the availability of high quality datasets. With this comes the need to accelerate and scale deep learning applications in order to...
    Resource Type:
    Masters Thesis
    Full Text:
    , W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei. Imagenet: A large- scale hierarchical image

  • Uncertainty in Deep Learning with Implicit Neural Networks

    Creator:
    Ratzlaff, Neale
    Abstract:
    The ability to extract uncertainties from predictions is crucial for the adoption of deep learning systems to safety-critical applications. Uncertainty estimates can be used as a failure signal, which is necessary for automating complex tasks where safety is a concern. Furthermore, current deep learning systems do not provide uncertainty estimates,...
    Resource Type:
    Dissertation
    Full Text:
    transforms z ∼ Z into latent codes {q1, . . . , qL}. The L generators each transform the latent subvector qi

  • Database transaction traffic algorithms

    Creator:
    Chang, Chung Wen
    Resource Type:
    Capstone Project
    Full Text:
    [1ys tem 11 IJ.1he n rnnr:s Lanc;ua e;e System 12 I. Q1F~'.L Datn Sublan gua g e 12 II. I NGTmS

  • Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome

    Creator:
    Yao, Yao
    Abstract:
    We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of...
    Resource Type:
    Dissertation
    Full Text:
    define, for the sets L ⊂ Z+ of numbered loci, S ⊂ Z+ of numbered SNPs, and R ⊂ S of numbered rSNPs. Then

  • Deep Learning for Human and Biological Languages

    Creator:
    Hong, Junki
    Abstract:
    We explore the application of deep learning to the disparate fields of natural language processing and computational biology. Both the sentences uttered by humans as well as the RNA and protein sequences found within the cells of their bodies can be considered formal languages in computer science, as sets of...
    Resource Type:
    Dissertation
    Full Text:
    system. Here ℓ is the step index, c and v are prefix and inside scores. Unlike Huang and Sagae (2010) [30

  • Knowledge Base Completion Leveraging Natural Language Data

    Creator:
    Shahbazi, Hamed
    Abstract:
    Information about named entities (real-world objects) is usually harvested from different sources and organized as a multiple relational directed graph in Knowledge Bases (KBs). KBs play essential roles in many NLP modules including question answering, fact-checking, search engines, etc. KBs are big but still incomplete: relational information among entities is...
    Resource Type:
    Dissertation
    Full Text:
    , ...yik} over context wi as ci = m∑ l=1 αilwil (2.2) The weight vector α is computed using the

  • Pixel- and Frame-level Video Labeling using Spatial and Temporal Convolutional Networks

    Creator:
    Lei, Peng
    Abstract:
    This dissertation addresses the problem of video labeling at both the frame and pixel levels using deep learning. For pixel-level video labeling, we have studied two problems: i) Spatiotemporal video segmentation and ii) Boundary detection and boundary flow estimation. For the problem of spatiotemporal video segmentation, we have developed recurrent...
    Resource Type:
    Dissertation
    Full Text:
    inputs and two outputs. The input represents feature sequences fL−11:T/2n and f L−1 1:T with temporal

  • Deep Convolutional Network on Point Clouds for 3D Scene Understanding

    Creator:
    Wu, Wenxuan
    Abstract:
    As one of the most popular data types, the point cloud is widely used in various appli- cations, including computer vision, computer graphics and robotics. The capability to directly measure 3D point clouds is invaluable in those applications as depth information could remove a lot of the segmentation ambiguities in...
    Resource Type:
    Dissertation
    Full Text:
    level l is concatenated with the upsampled feature from level l+ 1, which contains feature with a

  • Design and analysis of hard real-time systems

    Creator:
    Zhu, Jiang
    Abstract:
    First, we study hard real-time scheduling problems where each task is defined by a four tuple (r, c, p, d): r being its release time, c computation time, p period, and d deadline. The question is whether all tasks can meet their deadlines on one processor. If not, how many...
    Resource Type:
    Dissertation
    Full Text:
    to be n i - 1 scheduleable: (1) lc iip 1; (2) L z ci +Eflo OWL -1 )/pj)* c j, for all i = 2, ..., n

  • Adaptive Movement Intent Decoding for Intuitive Control of Neuroprostheses

    Creator:
    George, Damon A.
    Abstract:
    Movement intent decoders, which interpret volitional movement intent from human bioelectric signals, can be incorporated into modern neuroprostheses to offer people living with limb loss or paralysis the potential to regain their lost motor control. Machine learning methods have become the research standard for continuous decoders with high degrees of...
    Resource Type:
    Masters Thesis
    Full Text:
    -squares (LS) formulation. A batched gradient descent solution can easily be derived as follows. L (k) o

  • Detection and Segmentation of Bird Song in Noisy Environments

    Creator:
    Neal, Lawrence W.
    Abstract:
    Recent work in machine learning concerns the detection and identification of bird species from audio recordings of their vocalizations. Such analysis can yield valuable ecological information concerning the activity and distribution of species in the wild. Current species-identification methods require individual syllables of bird audio as input, but field-collected audio...
    Resource Type:
    Honors College Thesis
    Full Text:
    , Sarah Frey, Adam Hadley, and all the others who have made possible the research undertaken by the OSU

  • tCHARM : distributed concurrent processing using multithreading

    Creator:
    Ng, Avis
    Abstract:
    CHARM is a parallel programming language that was originally implemented for a network of workstations each of which has only one processor. In this project, we ported CHARM for a network of workstations each of which has more than one processor (multi-computer) using multithreading to exploit the multiple processors. Network...
    Resource Type:
    Capstone Project
    Full Text:
    directed to specific threads. Adam J. Ferrari and V.S. Sunderam built tPVM on top of PVM to allow spawning

  • Supervised Hierarchical Segmentation for Bird Bioacoustics

    Creator:
    Tjahja, Teresa V.
    Abstract:
    Bioacoustics analysis can be used to conduct environmental monitoring by detecting the presence of birds species. This analysis usually involves identifying the species from their calls. In most frameworks, bird song syllables are extracted from audio recordings and individual syllables are input to a classifier to identify the species. Extraction...
    Resource Type:
    Masters Thesis
    Full Text:
    like to thank Matthew Betts, Sarah Frey, Adam Hadley, and Jay Sexsmith for their help in collecting

  • Part-based and Uncertainty-Aware Few-shot Object Segmentation in Images

    Creator:
    Nguyen, Khoi D.
    Abstract:
    This dissertation addresses few-shot object segmentation in images. The goal of segmentation is to label every image pixel with a class of the object occupying that pixel, where the class may represent a semantic object category or instance. In few-shot segmentation, training and test datasets have different classes. Every new...
    Resource Type:
    Dissertation
    Full Text:
    weights, and the latter explicitly predicts uncertainty of predicting the left (L), right (R), top (T

  • A Study of Temporal Convolutional Neural Networks for Music Language Modeling as Applied to Automatic Music Transcription

    Creator:
    Meyn, Matthew
    Abstract:
    Automatic music transcription (AMT) is the task, given an acoustic representation of music, to recover a symbolic notation of the written notes expressed by the sound. Transcribing music with multiple notes sounding simultaneously is difficult for both humans and machines. Much existing work on AMT has focused on suitable acoustic...
    Resource Type:
    Capstone Project
    Full Text:
    the convolutional kernel as it is applied to its input. If F is a real-valued function over Z2, the l

  • Flex-shape convolution and an interactive image segmentation system

    Creator:
    Zhou, Zheng
    Abstract:
    Semantic image segmentation is a relatively difficult task in computer vision. With the advent of deep learning, semantic image segmentation is increasingly of interest for researchers because of the excellent predictions from Convolutional Neural Network (CNN). However, CNNs have proven to struggle with obtaining global context of image due to...
    Resource Type:
    Masters Thesis
    Full Text:
    merely embeds the functions in network one by one. Given a cross entropy cost function below: L(x, y

  • Deep Segmentation in Plant Phenomics

    Creator:
    Kaur, Damanpreet
    Abstract:
    The abilities of plant biologists to characterize the genetic basis of physiological traits are limited by their abilities to obtain quantitative data representing precise details of trait variation and mainly to collect this data on a high-throughput scale at low cost. Deep learning-based methods have demonstrated unprecedented potential to automate...
    Resource Type:
    Masters Thesis
    Full Text:
    the Adam Optimizer. We choose the initial learning rate of 1e-4 and use the bootstrapped cross

  • Image Based Crypto

    Creator:
    Basa, Gungor
    Abstract:
    The ability to create reproducible cryptographically secure keys from temporal environments (e.g., images) has the potential to be a contributor to effective cryptographic mechanisms. Due to the noisy nature of these environments, achieving this goal in a user friendly fashion is a very challenging task, especially since there exists a...
    Resource Type:
    Masters Thesis
    Full Text:
    respectively Let α, β, σb, σw, L, and DH represents False Acceptance Rate (FAR), False Rejection Rate (FRR

  • Towards Narrative Understanding with Deep Neural Networks and Hidden Markov Models

    Creator:
    Orr, J. Walker
    Abstract:
    Narratives are central to communication and the human experience. For a computer system to understand a narrative, it must be able to identify the key facts or plot elements that describe what happened or how the world has changed. These element are called events;establishing a document’s events and the relationships...
    Resource Type:
    Dissertation
    Full Text:
    ) (2.20) F1 = CNN(L0) (2.21) F2 = ReLU(F1) (2.22) F3 = CNN(F2) (2.23) L = normalize(L0 + F3) (2.24

  • Window Axial Vision Transformer for Image Classification

    Creator:
    Lee, Jisoo
    Abstract:
    Currently, a popular approach to image classification uses the deep Transformer architecture. In a Transformer, the attention mechanism enables the model to learn efficiently with fewer computational resources than the convolutional neural networks (CNNs). In this thesis, we study the sparse attention mechanism widely used in the Transformers developed specifically...
    Resource Type:
    Masters Thesis
    Full Text:
    , Yukun Zhu, Bradley Green, Hartwig Adam, Alan L. Yuille, and Liang- Chieh Chen. Axial-deeplab: Stand

  • A Study of Planar and Spherical Stereographics for Visualization

    Creator:
    AlSalehy, Ali S.
    Abstract:
    ”Until relatively recently, mankind was not aware that there was a separable binocular depth sense. Through the ages, people like Euclid and Leonardo understood that we see different images of the world with each eye. But it was Wheatstone who in 1838 explained to the world, with his stereoscope and...
    Resource Type:
    Capstone Project
    Full Text:
    Iskandrani, Hassan Lashari, Adam Zaid, Ziad Ldibry and whoever feels that he/she helped me under any

  • Explaining and Improving Neural Machine Translation

    Creator:
    Yang, Yilin
    Abstract:
    Machine Translation, the task of automatically translating between human languages has been studied for decades. This task is used to be solved by count-based statistical models, e.g. Phrase-based Statistical Machine Translation (PBSMT), which solves the translation problem by separately training a statistical language model and a translation model. Recently, Neural...
    Resource Type:
    Dissertation
    Full Text:
    length L(x, y) = min{|y|, gr · |x|}: ŜBWR(x, y) = S(x, y) + r · L(x, y) (3.6) 3.3.2 Rescoring with

  • Machine Learning for Precision Medicine: Application to Cancer Chemotherapy Response Prediction and Wound Healing Status Assessment

    Creator:
    Wei, Qi
    Abstract:
    Ph.D. candidate Qi Wei's thesis consists of two projects: Chemotherapy Project: a study based on the research paper "Predicting chemotherapy response of various cancer types using a variational auto-encoder approach" submitted to the bioRxiv preprint archive and accepted by the BMC bioinformatics; and Wound Monitor Project: implementing and assessing analytics...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . . . . . . 33 2.3.4 Machine learning models used for classification . . . . . . . . 36 2.3.5 Adam optimization

  • Methods for Detection and Recovery of Out-of-Distribution Examples

    Creator:
    Ratzlaff, Neale J.
    Abstract:
    Deep neural networks currently comprise the backbone of many applications where safety is a critical concern, for example: autonomous driving and medical diagnostics. Unfortunately these systems currently fail to detect out-of-distribution (OOD) inputs and can be prone to making dangerous errors when exposed to them. In addition, these same systems...
    Resource Type:
    Masters Thesis
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.4 Performance of BFNet against DeepFool and L-BFGS attacks. . . . . . . 40 3.5 Performance of

  • Visualization and design systems for road infrastructure

    Creator:
    Esch, Gregory
    Abstract:
    Transportation infrastructure provides a vital service for the functionality of a city. The efficient design of road networks poses an interesting topic in computer science for digital content developers. For civil engineers, the visualization of analysis results on infrastructure both efficiently and intuitively is crucial. The following contributions are made...
    Resource Type:
    Masters Thesis
    Full Text:
    Müller presented the most successful algorithm for the generation of street networks [48], extending L

  • A two level minimal perfect hash function for large data sets

    Creator:
    Barrar, Howard
    Abstract:
    Recently several minimal perfect hashing functions far small static word sets have been developed. However, they are limited to sets of 50 words or less. In this paper, a Two Level Minimal Perfect Hash Function for large data sets is given. It partitions a large static set into small sets...
    Resource Type:
    Capstone Project
    Full Text:
    L•vel Algorithm Step 1 of the algorithm partitions the large data set int o s ma 11 er s et s

  • Efficient training and feature induction in sequential supervised learning

    Creator:
    Hao, Guohua
    Abstract:
    Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
    Resource Type:
    Dissertation
    Full Text:
    , . . . , L}. Find: A classifier H that, given a new sequence X of feature vectors, predicts the

  • Painterly rendering using space-time varying style parameters

    Creator:
    Kagaya, Mizuki
    Abstract:
    Automatic painterly rendering systems have been proposed but they opted for selecting a single style to generate paintings from images, which lacks the ability of creatively using multiple styles to focus important objects and deemphasize unimportant part of the scenes. We provide a multi-style painting framework to address this issue...
    Resource Type:
    Masters Thesis

  • Robust and Efficient Classification of Videos in the Wild

    Creator:
    Mahasseni, Behrooz
    Abstract:
    Recognizing human actions in videos is a long-standing problem in computer vision with a wide range of applications including video surveillance, content retrieval, and sports analysis. This thesis focuses on addressing efficiency and robustness of video classification in unconstrained real-world settings. The thesis work can be broadly divided into four...
    Resource Type:
    Dissertation
    Full Text:
    with AdaGrad and Adam optimizers . . . . . . . . . . . . . . . . . 40 3.4 Per-class accuracy