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Laser Beams, Diffraction Gratings and Lenses

Laser Beams, Diffraction Gratings and Lenses

  • lindy-dunigan
  • 38 Slides

Zone plate. Laser-beam . diffraction. A lens tran...

Feature-Preserving Reconstruction of Singular Surfaces

Feature-Preserving Reconstruction of Singular Surfaces

  • tatiana-dople
  • 41 Slides

with . Tamal. . Dey. , . Qichao. . Que. , . Iss...

Statistical Integration of Time-lapse Seismic and Electroma

Statistical Integration of Time-lapse Seismic and Electroma

  • faustina-dinatale
  • 18 Slides

Jaehoon. Lee, . Tapan. . Mukerji. , Michael Tom...

CoBaFi : Collaborative Bayesian Filtering

CoBaFi : Collaborative Bayesian Filtering

  • stefany-barnette
  • 0 Slides

Alex Beutel. Joint work with Kenton Murray, . Chr...

CS5540: Computational Techniques for Analyzing Clinical Dat

CS5540: Computational Techniques for Analyzing Clinical Dat

  • natalia-silvester
  • 62 Slides

Lecture 7:. . Statistical Estimation: Least Squa...

ATLAS Forward

ATLAS Forward

  • phoebe-click
  • 0 Slides

Calorimeter. . Analysis . . Malika TOU...

Prime Numbers By Brian Stonelake

Prime Numbers By Brian Stonelake

  • olivia-moreira
  • 0 Slides

What’s a Prime Number?. Lots of definitions out...

Clustering Beyond K -means

Clustering Beyond K -means

  • phoebe-click
  • 0 Slides

David Kauchak. CS . 158. . – Fall . 2016. Admi...

THE CHIRPED-PULSE FOURIER TRANSFORM MICROWAVE (CP-FTMW) SPECTRUM AND

THE CHIRPED-PULSE FOURIER TRANSFORM MICROWAVE (CP-FTMW) SPECTRUM AND

  • natalia-silvester
  • 0 Slides

POTENTIAL ENERGY CALCULATIONS FOR AN AROMATIC CLA...

Value of I nformation Analysis in Spatial Models

Value of I nformation Analysis in Spatial Models

  • phoebe-click
  • 0 Slides

Jo Eidsvik. Jo.eidsvik@ntnu.no. My . background. ...

CSE 446: Expectation Maximization (EM)

CSE 446: Expectation Maximization (EM)

  • sherrill-nordquist
  • 0 Slides

Winter 2012. Daniel Weld. Slides adapted from Car...

New Algorithms for Heavy Hitters in Data Streams

New Algorithms for Heavy Hitters in Data Streams

  • sherrill-nordquist
  • 0 Slides

David Woodruff . IBM . Almaden. J. oint works wit...

History matching @ IDM Dan Klein, IDM Symposium,

History matching @ IDM Dan Klein, IDM Symposium,

  • debby-jeon
  • 0 Slides

4/18/2017. History Matching Software!. About. his...

Zurich SPM Course 2012 Spatial Preprocessing

Zurich SPM Course 2012 Spatial Preprocessing

  • trish-goza
  • 0 Slides

Ged Ridgway, London. With thanks to John Ashburne...

CSC589 Introduction to Computer Vision

CSC589 Introduction to Computer Vision

  • faustina-dinatale
  • 0 Slides

Lecture 6 . Image Derivative, Image-. Denoising. ...

Deutsches Elektronen Synchrotron DESY

Deutsches Elektronen Synchrotron DESY

  • phoebe-click
  • 0 Slides

Halo . Monitoring. = Very High Dynamic Beam Profi...

P r ob a b ilist i c R

P r ob a b ilist i c R

  • trish-goza
  • 0 Slides

obot. ics. B. ay. e. s. . Fil. t. er Im. p. leme...

Parallel Direct Methods for Sparse Linear Systems

Parallel Direct Methods for Sparse Linear Systems

  • tatiana-dople
  • 0 Slides

Contents. Problem Statement. Motivation. Types . ...

ARAPrototyper : Enabling Rapid Prototyping and Evaluation for

ARAPrototyper : Enabling Rapid Prototyping and Evaluation for

  • min-jolicoeur
  • 0 Slides

Accelerator-Rich . Architecture. Jason. . Cong,....

Pattern Recognition and

Pattern Recognition and

  • natalia-silvester
  • 0 Slides

Machine Learning. Chapter 1: Introduction. Examp...

Project 1 Hybrid Images A. Oliva, A. Torralba, P.G. Schyns,

Project 1 Hybrid Images A. Oliva, A. Torralba, P.G. Schyns,

  • mitsue-stanley
  • 0 Slides

“Hybrid Images,”. SIGGRAPH 2006. Why do we g...

Image gradients and edges

Image gradients and edges

  • stefany-barnette
  • 0 Slides

April 11. th. , 2017. Yong Jae Lee. UC Davis. Ann...

Pattern Recognition and

Pattern Recognition and

  • mitsue-stanley
  • 0 Slides

Machine Learning. Chapter 3: Linear models for r...

New Algorithms for Heavy Hitters in Data Streams

New Algorithms for Heavy Hitters in Data Streams

  • pasty-toler
  • 0 Slides

David Woodruff . IBM . Almaden. J. oint works wit...

CSE 554 Lecture 6: Fairing

CSE 554 Lecture 6: Fairing

  • alida-meadow
  • 0 Slides

Fall 2016. Review. Iso. -contours in grayscale im...

Support vector machines When the data is linearly separable, which of the many possible solutions s

Support vector machines When the data is linearly separable, which of the many possible solutions s

  • alida-meadow
  • 0 Slides

SVM criterion: maximize the . margin. , or distan...

Kunal Talwar MSR SVC The Price of Privacy and

Kunal Talwar MSR SVC The Price of Privacy and

  • luanne-stotts
  • 0 Slides

the Limits of LP decoding. [. Dwork, McSherry, Ta...

How to use Gaussian on Joker

How to use Gaussian on Joker

  • tatiana-dople
  • 0 Slides

Presented by: CIA/HPC Team . Feb 10, 2017. Copy G...

All about convolution Last time: Convolution and cross-correlation

All about convolution Last time: Convolution and cross-correlation

  • alexa-scheidler
  • 0 Slides

Cross correlation. Convolution. Last time: Convol...

Reader’s Guide to Pattern Recognition

Reader’s Guide to Pattern Recognition

  • mitsue-stanley
  • 0 Slides

& . Machine Learning. George Nagy. Professor...

Interest points CSE P 576

Interest points CSE P 576

  • danika-pritchard
  • 0 Slides

Larry Zitnick (. larryz@microsoft.com. ). Many sl...

Linear Filters April 6 th

Linear Filters April 6 th

  • alida-meadow
  • 0 Slides

, 2017. Yong Jae Lee. UC Davis. Announcements. PS...

Uncertainty Representation

Uncertainty Representation

  • danika-pritchard
  • 0 Slides

Gaussian Distribution. variance. Standard deviati...

Sparsified Matrix Algorithms for Graph Laplacians

Sparsified Matrix Algorithms for Graph Laplacians

  • lois-ondreau
  • 0 Slides

Richard Peng. Georgia Tech. OUtline. (Structured)...

A Tale of Two Careers (tightly intertwined)

A Tale of Two Careers (tightly intertwined)

  • danika-pritchard
  • 0 Slides

Andrew J. Viterbi. Presidential Chair Professor o...

A Tour of Image Denoising

A Tour of Image Denoising

  • faustina-dinatale
  • 0 Slides

Shahar . Kovalsky. Alon. . Faktor. 17/4/2011. IR...

Representation of synchrotron radiation in phase space

Representation of synchrotron radiation in phase space

  • marina-yarberry
  • 0 Slides

Ivan . Bazarov. 1. Outline. Motion in phase space...

CS448f: Image Processing For Photography and Vision

CS448f: Image Processing For Photography and Vision

  • calandra-battersby
  • 0 Slides

Fast Filtering. Problems in Computer Vision. Comp...

Kalman Filtering ECE 383 / MEMS 442: Introduction to Robotics

Kalman Filtering ECE 383 / MEMS 442: Introduction to Robotics

  • natalia-silvester
  • 0 Slides

Kris Hauser. Agenda. Introduction to sensing and ...

TxMiner : Identifying Transmitters in Real World Spectrum Measurements

TxMiner : Identifying Transmitters in Real World Spectrum Measurements

  • pamella-moone
  • 0 Slides

Mariya . Zheleva. University at Albany, SUNY. Spe...

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