'Our algorithm' presentation slideshows

Randomized Algorithms for Bayesian Hierarchical Clustering

Randomized Algorithms for Bayesian Hierarchical Clustering. Katherine A. Heller Zoubin Ghahramani Gatsby Unit, University College London. Hierarchies:. are natural outcomes of certain generative processes are intuitive representations for certain kinds of data Examples:

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Delaunay Meshing for Piecewise Smooth Complexes

Delaunay Meshing for Piecewise Smooth Complexes. Tamal K. Dey The Ohio State U. Joint work: Siu-Wing Cheng, Joshua Levine, Edgar A. Ramos. Sharp Edges. Non-manifold. Piecewise Smooth Complexes. Piecewise Smooth Complexes. D is a piecewise smooth complex (PSC) if

577 views • 46 slides

Visibility-Guided Simplification

Visibility-Guided Simplification. Eugene Zhang and Greg Turk GVU Center, College of Computing Georgia Institute of Technology. Courtesy of Nooruddin and Turk. Introduction. Problem: Use visibility information to guide simplification. . Why useful :. Introduction. Solution:

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An Implementation of Mostly-Copying GC on Ruby VM

An Implementation of Mostly-Copying GC on Ruby VM. Tomoharu Ugawa The University of Electro-Communications, Japan. Background(1/2). Script languages are used at various scene Before: only for tiny applications Short lifetime Runs with little memory

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An Implementation of Mostly-Copying GC on Ruby VM

An Implementation of Mostly-Copying GC on Ruby VM. Tomoharu Ugawa The University of Electro-Communications, Japan. Background(1/2). Script languages are used at various scene Before: only for tiny applications Short lifetime Runs with little memory

429 views • 35 slides

Active Random Fields

Active Random Fields. Adrian Barbu. The MAP Estimation Problem. Estimation problem: Given input data y, solve Example: Image denoising Given noisy image y , find denoised image x Issues Modeling : How to approximate ? Computing : How to find x fast?. Noisy image y.

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Amortized Supersampling

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Performance guarantees for hierarchical clustering

Performance guarantees for hierarchical clustering. Sanjoy Dasgupta University of California, San Diego Philip Long Genomics Institute of Singapore. 1- clustering. 2- clustering. 3- clustering. 4- clustering. 5- clustering. 1 2 3 4 5.

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Applying Support Vector Machines to Imbalanced Datasets

Applying Support Vector Machines to Imbalanced Datasets. Authors: Rehan Akbani, Stephen Kwek (University of Texas at San Antonio, USA) Nathalie Japkowicz (University of Ottawa, Canada) Published: European Conference on Machine Learning (ECML), 2004 Presenter: Rehan Akbani

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Fast Iterative Alignment of Pose Graphs with Poor Initial Estimates

Fast Iterative Alignment of Pose Graphs with Poor Initial Estimates. Edwin Olson eolson@mit.edu John Leonard, Seth Teller jleonard@mit.edu, teller@csail.mit.edu http://rvsn.csail.mit.edu/graphoptim. Where are we going?. +. =. Feature. Pose. Constraint. Problem summary.

467 views • 24 slides

Student: Shihyu Tsai Advisor: Chiuyuan Chen

An efficient self-stabilizing algorithm for the minimal dominating set problem under a distributed scheduler . Student: Shihyu Tsai Advisor: Chiuyuan Chen. Department of Applied Mathematics National Chiao Tung University. Outline. Introduction History Our Algorithm An example

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Overview of the Synthetic Derivative

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Initial Operational Capability (IOC) Implementation at MSC

Initial Operational Capability (IOC) Implementation at MSC. Richard Hogue, Operations Branch, Canadian Meteorological Center. Second North American Ensemble Forecast System Workshop NCEP November 16-18, 2004. Canadian Meteorological Center Ensemble Prediction System (EPS).

404 views • 18 slides

Theoretical Analysis

Theoretical Analysis. Objective. Our algorithm use some kind of hashing technique, called random projection . In this slide, we will show that if a user want to find motif with high chance, how many non-motif will be occur in hashing process.

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Leveraging Social Media Data as Real-Time Indicators of X

Leveraging Social Media Data as Real-Time Indicators of X. September 7, 2012. Outline. Opportunities and Challenges in Big Data Sentiment Tracking Methods Validating Results Case Study: Heat Mapping Case Study- Economic forecasting. The Energy Market as a Metaphor. Big Data . Energy.

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Accelerated Stereoscopic Rendering using GPU

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CANDID.CLICKS

CANDID.CLICKS. NYU: WEB DEVELOPMENT & MOBILE APPS ; SPRING 2014. THE TRUTH IS WE FORGET. S ometimes we forget to capture the best memories; the candid ones. OUR SOLUTION. HOW DOES IT WORK?. SELECT A START TIME. HOW DOES IT WORK?. 2. SELECT A STOP TIME. HOW DOES IT WORK?.

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Seizure Detection

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Our Contribution: Finding Communities What are the features of human communities?

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