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  • Machine learning for computer architecture design and optimization

    O Criador:
    Penney, Drew
    Abstract:
    Machine learning applied to computer architecture has rapidly transitioned from a theoretical novelty to being a driving force behind design, control, and simulation in practically all components. These machine-learning-based methodologies are further notable for their scalability to increasingly complex design challenges, which has allowed these methodologies to surpass the prior...
    Resource Type:
    Dissertation

  • Investigating Transcriptional Control of Specialized Gene Expression in Plants

    O Criador:
    Fraser, Valerie N.
    Abstract:
    Specialized or secondary metabolism is a collection of pathways and small molecules that, while beneficial to an organism, are not strictly necessary for survival. Plants use secondary metabolites to, among other things, attract pollinators, defend against biotic and abiotic stressors, and form symbioses. Natural products from plants have seen an...
    Resource Type:
    Dissertation

  • Representationally Robust and Scalable Learning over Relational Databases

    O Criador:
    Picado Leiva, Jose Manuel
    Abstract:
    Learning novel concepts from relational databases is an important problem with applications in several disciplines, such as data management, natural language processing, and bioinformatics. For a learning algorithm to be effective, the input data should be clean and in some desired representation. However, real-world data is usually heterogeneous – the...
    Resource Type:
    Dissertation

  • Anomaly Detection: Theory, Explanation and User Feedback

    O Criador:
    Siddiqui, Md Amran
    Abstract:
    Anomaly detection has been used in variety of applications in practice, including cyber-security, fraud detection and detecting faults in safety critical systems, etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most...
    Resource Type:
    Dissertation

  • Lamb Wave Mode Decomposition and Its Applications in Structural Health Monitoring

    O Criador:
    Zoubi, Ahmad Bassil
    Abstract:
    Structural health monitoring (SHM) systems perform automated non-destructive damage detection and characterization for a variety of large structures including civil structures such as bridges and aerospace structures such as aircrafts and space vehicles. The goals of SHM include preventing catastrophic structural failures, increasing reliability, reducing maintenance costs, and increasing the...
    Resource Type:
    Dissertation

  • Localized Variable Selection with Random Forest

    O Criador:
    Niyaghi, Faraz
    Abstract:
    Due to recent advances in computer technology, the cost of collecting and storing data has dropped drastically. This makes it feasible to collect large amounts of information for each data point. This increasing trend in feature dimensionality justifies the need for research on variable selection. Random forest (RF) has demonstrated...
    Resource Type:
    Dissertation

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

    O Criador:
    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

  • Weak-supervision : Probabilistic Models and Inference

    O Criador:
    Pham, Anh
    Abstract:
    In supervised learning, label information can be provided at different levels of granularity. For small datasets, it is possible to acquire a label for each data instance. However, in the big-data regime, this fine granularity approach is prohibitively costly. For example, in semi-supervised learning, only a limited number of samples...
    Resource Type:
    Dissertation

  • Automated Assembly Planning : From CAD Model to Virtual Assembly Process

    O Criador:
    Rafibakhsh, Nima
    Abstract:
    Assembly planning is a crucial task for every manufacturing product. In general, assembly operations consume more than 30% of the total manufacturing time and cost. Therefore, any effort in optimizing assembly will have a significant impact on the economic success of manufacturing. Finding an optimal assembly plan by hand is...
    Resource Type:
    Dissertation

  • Parsing with Recurrent Neural Networks

    O Criador:
    Cross, James Henry, III
    Abstract:
    Machine learning models for natural language processing have traditionally relied on large numbers of discrete features, built up from atomic categories such as word forms and part-of-speech labels, which are considered completely distinct from each other. Recently however, the advent of dense feature representations coupled with deep learning techniques has...
    Resource Type:
    Dissertation