Now showing items 715-734 of 1175

    • Network-based visual analysis of tabular data 

      Liu, Zhicheng (Georgia Institute of Technology, 2012-04-04)
      Tabular data is pervasive in the form of spreadsheets and relational databases. Although tables often describe multivariate data without explicit network semantics, it may be advantageous to explore the data modeled as a ...
    • Neural Methods for Resolving Hard-to-Predict Branches 

      Gupta, Pulkit (Georgia Institute of Technology, 2021-12-17)
      This work presents a new category of branch predictors designed to be addendums to existing state of the art prediction mechanisms. We call these neural network inspired predictors Shallow Online Neural (SON) Predictors ...
    • A neurally based vision model for line extraction and attention 

      Ungruh, Joachim (Georgia Institute of Technology, 1992)
    • Neuro-general computing an acceleration-approximation approach 

      Yazdan Bakhsh, Amir (Georgia Institute of Technology, 2018-07-30)
      A growing number of commercial and enterprise systems rely on compute and power intensive tasks. While the demand of these tasks is growing, the performance benefits from general-purpose platforms are diminishing. Without ...
    • Neurosymbolic automated story generation 

      Martin, Lara Jean (Georgia Institute of Technology, 2021-04-07)
      Although we are currently riding a technological wave of personal assistants, many of these agents still struggle to communicate appropriately. Humans are natural storytellers, so it would be fitting if artificial intelligence ...
    • New abstractions and mechanisms for virtualizing future many-core systems 

      Kumar, Sanjay (Georgia Institute of Technology, 2008-07-08)
      To abstract physical into virtual computing infrastructures is a longstanding goal. Efforts in the computing industry started with early work on virtual machines in IBM's VM370 operating system and architecture, continued ...
    • New approaches to integer programming 

      Chandrasekaran, Karthekeyan (Georgia Institute of Technology, 2012-06-28)
      Integer Programming (IP) is a powerful and widely-used formulation for combinatorial problems. The study of IP over the past several decades has led to fascinating theoretical developments, and has improved our ability to ...
    • New constructions of cryptographic pseudorandom functions 

      Banerjee, Abhishek (Georgia Institute of Technology, 2015-07-02)
      Pseudorandom functions (PRFs) are the building blocks of symmetric-key cryptography. Almost all central goals of symmetric cryptography (e.g., encryption, authentication, identification) have simple solutions that make ...
    • New Directions in Garbled Circuits 

      Heath, David Anthony (Georgia Institute of Technology, 2022-05-02)
      The Garbled Circuit (GC) technique is foundational in secure multiparty computation (MPC). GC allows parties to jointly and securely compute functions of their private inputs while revealing nothing but the output. GC is ...
    • New formulations for active learning 

      Ganti Mahapatruni, Ravi Sastry (Georgia Institute of Technology, 2014-01-10)
      In this thesis, we provide computationally efficient algorithms with provable statistical guarantees, for the problem of active learning, by using ideas from sequential analysis. We provide a generic algorithmic framework ...
    • New insights on the power of active learning 

      Berlind, Christopher (Georgia Institute of Technology, 2015-07-22)
      Traditional supervised machine learning algorithms are expected to have access to a large corpus of labeled examples, but the massive amount of data available in the modern world has made unlabeled data much easier to ...
    • New paradigms for approximate nearest-neighbor search 

      Ram, Parikshit (Georgia Institute of Technology, 2013-07-02)
      Nearest-neighbor search is a very natural and universal problem in computer science. Often times, the problem size necessitates approximation. In this thesis, I present new paradigms for nearest-neighbor search (along with ...
    • New results on discrete-time time-varying linear systems 

      Hafiz, Khaled Mohammad (Georgia Institute of Technology, 1975)
    • New support vector machine formulations and algorithms with application to biomedical data analysis 

      Guan, Wei (Georgia Institute of Technology, 2011-06-13)
      The Support Vector Machine (SVM) classifier seeks to find the separating hyperplane wx=r that maximizes the margin distance 1/||w||2^2. It can be formalized as an optimization problem that minimizes the hinge loss Ʃ[subscript ...
    • New tools for unsupervised learning 

      Xiao, Ying (Georgia Institute of Technology, 2014-08-21)
      In an unsupervised learning problem, one is given an unlabelled dataset and hopes to find some hidden structure; the prototypical example is clustering similar data. Such problems often arise in machine learning and ...
    • NEWS DATA VISUALIZATION INTERFACE DEVELOPMENT USING NMF ALGORITHM 

      Ahn, Byeongsoo (Georgia Institute of Technology, 2022-05-03)
      News data is a super large-scale dataset. It covers a wide range of topics ranging from heavy topics such as politics and society to beauty and entertainment, relatively light topics. At the same time, it is also the most ...
    • Non-photorealistic rendering with coherence for augmented reality 

      Chen, Jiajian (Georgia Institute of Technology, 2012-07-16)
      A seamless blending of the real and virtual worlds is key to increased immersion and improved user experiences for augmented reality (AR). Photorealistic and non-photorealistic rendering (NPR) are two ways to achieve this ...
    • Nonnegative matrix and tensor factorizations, least squares problems, and applications 

      Kim, Jingu (Georgia Institute of Technology, 2011-11-14)
      Nonnegative matrix factorization (NMF) is a useful dimension reduction method that has been investigated and applied in various areas. NMF is considered for high-dimensional data in which each element has a nonnegative ...
    • Nonnegative matrix factorization for clustering 

      Kuang, Da (Georgia Institute of Technology, 2014-07-01)
      This dissertation shows that nonnegative matrix factorization (NMF) can be extended to a general and efficient clustering method. Clustering is one of the fundamental tasks in machine learning. It is useful for unsupervised ...
    • Novel document representations based on labels and sequential information 

      Kim, Seungyeon (Georgia Institute of Technology, 2015-07-23)
      A wide variety of text analysis applications are based on statistical machine learning techniques. The success of those applications is critically affected by how we represent a document. Learning an efficient document ...