Now showing items 21-35 of 35

    • PHAST: Hardware-Accelerated Shortest Path Trees 

      Delling, Daniel (Georgia Institute of Technology, 2011-02-25)
      We present a novel algorithm to solve the nonnegative single-source shortest path problem on road networks and other graphs with low highway dimension. After a quick preprocessing phase, we can compute all distances from ...
    • Modeling Rich Structured Data via Kernel Distribution Embeddings 

      Song, Le (Georgia Institute of Technology, 2011-03-25)
      Real world applications often produce a large volume of highly uncertain and complex data. Many of them have rich microscopic structures where each variable can take values on manifolds (e.g., camera rotations), combinatorial ...
    • Multicore-oblivious Algorithms 

      Chowdhury, Rezaul Alam (Georgia Institute of Technology, 2011-03-28)
      Multicores represent a paradigm shift in general-purpose computing away from the von Neumann model to a collection of cores on a chip communicating through a cache hierarchy under a shared memory. Designing efficient ...
    • Mining Billion-Node Graphs: Patterns, Generators, and Tools 

      Faloutsos, Christos (Georgia Institute of Technology, 2011-04-08)
      What do graphs look like? How do they evolve over time? How to handle a graph with a billion nodes? We present a comprehensive list of static and temporal laws, and some recent observations on real graphs (like, e.g., ...
    • Optimization for Machine Learning: SMO-MKL and Smoothing Strategies 

      Vishwanathan, S. V. N. (Georgia Institute of Technology, 2011-04-15)
      Our objective is to train $p$-norm Multiple Kernel Learning (MKL) and, more generally, linear MKL regularised by the Bregman divergence, using the Sequential Minimal Optimization (SMO) algorithm. The SMO algorithm is simple, ...
    • Spatial Stochastic Simulation of Polarization in Yeast Mating 

      Petzold, Linda (Georgia Institute of Technology, 2011-04-19)
      In microscopic systems formed by living cells, the small numbers of some reactant molecules can result in dynamical behavior that is discrete and stochastic rather than continuous and deterministic. Spatio-temporal gradients ...
    • Coordinate Sampling for Sublinear Optimization and Nearest Neighbor Search 

      Clarkson, Kenneth L. (Georgia Institute of Technology, 2011-04-22)
      I will describe randomized approximation algorithms for some classical problems of machine learning, where the algorithms have provable bounds that hold with high probability. Some of our algorithms are sublinear, that is, ...
    • Graphical Models for the Internet 

      Smola, Alexander (Georgia Institute of Technology, 2011-04-29)
      In this talk I will present algorithms for performing large scale inference using Latent Dirichlet Allocation and a novel Cluster-Topic model to estimate user preferences and to group stories into coherent, topically ...
    • How much (execution) time and energy does my algorithm cost? 

      Vuduc, Richard W. (Georgia Institute of Technology, 2012-08-24)
      When designing an algorithm or performance-tuning code, is time-efficiency (e.g., operations per second) the same as energy-efficiency (e.g., operations per Joule)? Why or why not? To answer these questions, we posit a ...
    • High-performance-computing challenges for heart simulations 

      Fenton, Flavio H. (Georgia Institute of Technology, 2012-08-31)
      The heart is an electro-mechanical system in which, under normal conditions, electrical waves propagate in a coordinated manner to initiate an efficient contraction. In pathologic states, propagation can destabilize and ...
    • Stochastic Gradient Descent with Only One Projection 

      Jin, Rong (Georgia Institute of Technology, 2012-09-28)
      Although many variants of stochastic gradient descent have been proposed for large-scale convex optimization, most of them require projecting the solution at {\it each} iteration to ensure that the obtained solution stays ...
    • Magnetic Resonance Imaging of the Brain 

      Hu, Xiaoping (Georgia Institute of Technology, 2012-10-12)
      Magnetic Resonance Imaging (MRI) has become a powerful, indispensable, and ubiquitously used methodology in neuroimaging. In particularly, functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) ...
    • Extending Hadoop to Support Binary-Input Applications 

      Hong, Bo (Georgia Institute of Technology, 2012-10-19)
      Many data-intensive applications naturally take multiple inputs, which is not well supported by some popular MapReduce implementations, such as Hadoop. In this talk, we present an extension of Hadoop to better support such ...
    • Cyber Games 

      Vorobeychik, Yevgeniy (Georgia Institute of Technology, 2013-02-19)
      Over the last few years I have been working on game theoretic models of security, with a particular emphasis on issues salient in cyber security. In this talk I will give an overview of some of this work. I will first spend ...
    • The Aha! Moment: From Data to Insight 

      Shahaf, Dafna (Georgia Institute of Technology, 2014-02-07)
      The amount of data in the world is increasing at incredible rates. Large-scale data has potential to transform almost every aspect of our world, from science to business; for this potential to be realized, we must turn ...