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Deep surface and strategic approaches to learning Deep surface and strategic approaches to learning Contributor Jackie Lublin Centre for Teaching and Learning Good Practice in Teaching and Learning

Deep surface and strategic approaches to learning Deep surface and strategic approaches to learning Contributor Jackie Lublin Centre for Teaching and Learning Good Practice in Teaching and Learning

  • pamella-moone
  • 11 Slides

It is quite likely that the way you answer this h...

Angular Kinematics Learning Objectives Define angular kinematics Understand compute absolute relative angles angular displacements velocities accelerations Estimate instantaneous angular velocity

Angular Kinematics Learning Objectives Define angular kinematics Understand compute absolute relative angles angular displacements velocities accelerations Estimate instantaneous angular velocity

  • sherrill-nordquist
  • 9 Slides

Be able to identify phases of movement infer sou...

On the importance of initialization and momentum in deep learning Ilya Sutskever ilyasugoogle

On the importance of initialization and momentum in deep learning Ilya Sutskever ilyasugoogle

  • faustina-dinatale
  • 9 Slides

com James Martens jmartenscstorontoedu George Dahl...

Reinforcement Learning Neural Networks and PI Control Applied to a Heating Coil Charles W

Reinforcement Learning Neural Networks and PI Control Applied to a Heating Coil Charles W

  • conchita-marotz
  • 8 Slides

Anderson Douglas C Hittle Alon D Katz and R Ma...

Synthesis of Reinforcement Learning Neural Networks and PI Control Applied to a Simulated Heating Coil Charles W

Synthesis of Reinforcement Learning Neural Networks and PI Control Applied to a Simulated Heating Coil Charles W

  • karlyn-bohler
  • 15 Slides

Anderson Douglas C Hittle Alon D Katz and R Ma...

Abstract This article presents a range of possible values for the fault resistance in transmission power systems considering six existing models for the arc resistance and a model for the grounding i

Abstract This article presents a range of possible values for the fault resistance in transmission power systems considering six existing models for the arc resistance and a model for the grounding i

  • stefany-barnette
  • 8 Slides

Resistance by possible additional objects in the ...

Statistics Introduction to ARMA Models Overview

Statistics Introduction to ARMA Models Overview

  • lois-ondreau
  • 15 Slides

Modeling paradigm 2 Review stationary linear proc...

Lecture ARMA Models Bus Autumn Quarter Mr

Lecture ARMA Models Bus Autumn Quarter Mr

  • test
  • 8 Slides

Ruey S Tsay Autoregressive MovingAverage ARMA mod...

topicmodels An Package for Fitting Topic Models Bettina Gr un Johannes Kepler Universit at Linz Kurt Hornik WU Wirtschaftsuniversit at Wien Abstract This article is a slightly modied and shortened v

topicmodels An Package for Fitting Topic Models Bettina Gr un Johannes Kepler Universit at Linz Kurt Hornik WU Wirtschaftsuniversit at Wien Abstract This article is a slightly modied and shortened v

  • natalia-silvester
  • 20 Slides

The 64257tted model can be used to estimate the s...

Partbased models for nding people and estimating their pose Deva Ramanan Abstract This chapter will survey approaches to person detection and pose esti mation with the use of partbased models

Partbased models for nding people and estimating their pose Deva Ramanan Abstract This chapter will survey approaches to person detection and pose esti mation with the use of partbased models

  • yoshiko-marsland
  • 25 Slides

After a brief introductionmotivation for the need...

D eople racking with Gaussian Pr ocess Dynamical Models Raquel Urtasun Computer ision Laboratory EPFL Switzerland raquel

D eople racking with Gaussian Pr ocess Dynamical Models Raquel Urtasun Computer ision Laboratory EPFL Switzerland raquel

  • trish-goza
  • 8 Slides

urtasunep64258ch Da vid J Fleet Dept of Computer S...

Online Learned Discriminative PartBased Appearance Models for MultiHuman Tracking Bo Yang and Ram Nevatia Institute for Robotics and Intelligent Systems University of Southern California Los Angeles

Online Learned Discriminative PartBased Appearance Models for MultiHuman Tracking Bo Yang and Ram Nevatia Institute for Robotics and Intelligent Systems University of Southern California Los Angeles

  • natalia-silvester
  • 14 Slides

edu Abstract We introduce an online learning appro...

IEEE International Conference on Computer Vision ICCV N ovember Barcelona Spain

IEEE International Conference on Computer Vision ICCV N ovember Barcelona Spain

  • debby-jeon
  • 8 Slides

Latent Structured Models for Human Pose Estimatio...

Chapter Part II Autoregressive Models Another simple time series model is the f irst order autoregression denoted by AR

Chapter Part II Autoregressive Models Another simple time series model is the f irst order autoregression denoted by AR

  • calandra-battersby
  • 5 Slides

Th eries is AR1 if it satis64257es the iterativ...

Estimation of Parameters and Eigenmodes of Multivariate Autoregressive Models ARNOLD NEUMAIER Universitt Wien and TAPIO SCHNEIDER New York University Dynamical characteristics of a complex system can

Estimation of Parameters and Eigenmodes of Multivariate Autoregressive Models ARNOLD NEUMAIER Universitt Wien and TAPIO SCHNEIDER New York University Dynamical characteristics of a complex system can

  • olivia-moreira
  • 31 Slides

Oscillations in geophysical systems for example a...

A Direct Adaptive Method for Faster Backpropagation Learning The RPROP Algorithm Martin Riedmiller Heinrich Braun Institut fur Logik Komplexitat und Deduktionssyteme University of Karlsruhe W Karlsru

A Direct Adaptive Method for Faster Backpropagation Learning The RPROP Algorithm Martin Riedmiller Heinrich Braun Institut fur Logik Komplexitat und Deduktionssyteme University of Karlsruhe W Karlsru

  • luanne-stotts
  • 6 Slides

ukade Abstract A new learning algorithm for multi ...

Characterization of architectural tree models using Lsystems and Petri nets Przemyslaw Prusinkiewicz and William R

Characterization of architectural tree models using Lsystems and Petri nets Przemyslaw Prusinkiewicz and William R

  • celsa-spraggs
  • 20 Slides

Remphrey Department of Computer Science Universit...

Perceptual illusions and brain models Perceptual illusions and brain models R

Perceptual illusions and brain models Perceptual illusions and brain models R

  • pasty-toler
  • 8 Slides

L Gregory Reprinted from Proc Royal Society B 171...

Filter Forests for Learning DataDependent Convolutional Kernels Sean Ryan Fanello Cem Keskin Pushmeet Kohli Shahram Izadi Jamie Shotton Antonio Criminisi Ugo Pattacini Tim Paek Microsoft Research iC

Filter Forests for Learning DataDependent Convolutional Kernels Sean Ryan Fanello Cem Keskin Pushmeet Kohli Shahram Izadi Jamie Shotton Antonio Criminisi Ugo Pattacini Tim Paek Microsoft Research iC

  • liane-varnes
  • 8 Slides

FF can be used for general signal restoration tas...

Cascade Object Detection with Deformable Part Models Pedro F

Cascade Object Detection with Deformable Part Models Pedro F

  • calandra-battersby
  • 8 Slides

Felzenszwalb University of Chicago pffcsuchicagoe...

Learning a CategoryIndependent Object Detection Cascade Esa Rahtu Juho Kannala Machine Vision Group University of Oulu Finland Matthew Blaschko Visual Geometry Group University of Oxford UK Abstract

Learning a CategoryIndependent Object Detection Cascade Esa Rahtu Juho Kannala Machine Vision Group University of Oulu Finland Matthew Blaschko Visual Geometry Group University of Oxford UK Abstract

  • briana-ranney
  • 8 Slides

Here we focus on the 64257rst layers of a categor...

MultipleInstance Pruning For Learning Efcient Cascade Detectors Cha Zhang and Paul Viola Microsoft Research One Microsoft Way Redmond WA chazhangviola microsoft

MultipleInstance Pruning For Learning Efcient Cascade Detectors Cha Zhang and Paul Viola Microsoft Research One Microsoft Way Redmond WA chazhangviola microsoft

  • liane-varnes
  • 8 Slides

com Abstract Cascade detectors have been shown to ...

Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning Adam Coates Blake Carpenter Carl Case Sanjeev Satheesh Bipin Suresh Tao Wang David J

Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning Adam Coates Blake Carpenter Carl Case Sanjeev Satheesh Bipin Suresh Tao Wang David J

  • karlyn-bohler
  • 6 Slides

Wu Andrew Y Ng Computer Science Department Stanfo...

Journal of Machine Learning Research Submitted Published SupportVectorClustering Asa BenHur asabarnhilltechnologies

Journal of Machine Learning Research Submitted Published SupportVectorClustering Asa BenHur asabarnhilltechnologies

  • tawny-fly
  • 13 Slides

com BIOwulf Technologies 2030 Addison st suite 102...

CHAPTER GENERATIVE AND DISCRIMINATIVE CLASSIFIERS NAIVE BAYES AND LOGISTIC REGRESSION Machine Learning Copyright

CHAPTER GENERATIVE AND DISCRIMINATIVE CLASSIFIERS NAIVE BAYES AND LOGISTIC REGRESSION Machine Learning Copyright

  • faustina-dinatale
  • 17 Slides

Tom M Mitchell All rights reserved DRAFT OF Janua...

PN Learning Bootstrapping Binary Classiers by Structural Constraints Zdenek Kalal University of Surrey Guildford UK z

PN Learning Bootstrapping Binary Classiers by Structural Constraints Zdenek Kalal University of Surrey Guildford UK z

  • tatiana-dople
  • 8 Slides

kalalsurreyacuk Jiri Matas Czech Technical Univers...

CoarsetoFine Inference and Learning for FirstOrder Probabilistic Models Chlo e Kiddon and Pedro Domingos Department of Computer Science Engineering University of Washington Seattle WA chloepedrod c

CoarsetoFine Inference and Learning for FirstOrder Probabilistic Models Chlo e Kiddon and Pedro Domingos Department of Computer Science Engineering University of Washington Seattle WA chloepedrod c

  • min-jolicoeur
  • 8 Slides

washingtonedu Abstract Coarseto64257ne approaches ...

Using feedback to help students to learn Phil Race Summary Feedback is vital in just about all learning contexts

Using feedback to help students to learn Phil Race Summary Feedback is vital in just about all learning contexts

  • alexa-scheidler
  • 11 Slides

These p ages look at the central role that feedba...

Learning Stochastic Feedforward Neural Networks Yichuan Tang Department of Computer Science University of Toronto Toronto Ontario Canada

Learning Stochastic Feedforward Neural Networks Yichuan Tang Department of Computer Science University of Toronto Toronto Ontario Canada

  • luanne-stotts
  • 9 Slides

tangcstorontoedu Ruslan Salakhutdinov Department ...

A Volumetric Method for Building Complex Models from Range Images Brian Curless and Marc Levoy Stanford University Abstract A number of techniques have been developed for reconstructing sur faces by

A Volumetric Method for Building Complex Models from Range Images Brian Curless and Marc Levoy Stanford University Abstract A number of techniques have been developed for reconstructing sur faces by

  • phoebe-click
  • 10 Slides

A desirable set of properties for such algorithms...

Learning realistic human actions from movies Ivan Laptev Marcin Marszaek Cordelia Schmid Benjamin Rozenfeld INRIA Rennes IRISA INRIA Grenoble LEAR LJK BarIlan University ivan

Learning realistic human actions from movies Ivan Laptev Marcin Marszaek Cordelia Schmid Benjamin Rozenfeld INRIA Rennes IRISA INRIA Grenoble LEAR LJK BarIlan University ivan

  • ellena-manuel
  • 8 Slides

laptevinriafr marcinmarszalekinriafr cordeliaschmi...

Computational methods for mixed models Douglas Bates Department of Statistics University of Wisconsin Madison June Abstract The lme package provides R functions to t and analyze several dierent ty

Computational methods for mixed models Douglas Bates Department of Statistics University of Wisconsin Madison June Abstract The lme package provides R functions to t and analyze several dierent ty

  • alida-meadow
  • 21 Slides

In this vignette we describe the formulation of t...

JOURNAL OF MEMORY AND LANGUAGE Some Criticisms of Connectionist Models of Human Performance DOMINIC W

JOURNAL OF MEMORY AND LANGUAGE Some Criticisms of Connectionist Models of Human Performance DOMINIC W

  • lindy-dunigan
  • 22 Slides

MASSARO Program in Experimentul Psychology Univer...

Rectier Nonlinearities Improve Neural Network Acoustic Models Andrew L

Rectier Nonlinearities Improve Neural Network Acoustic Models Andrew L

  • faustina-dinatale
  • 6 Slides

Maas amaascsstanfordedu Awni Y Hannun awnicsstanf...

UAV Cooperative Control with Stochastic Risk Models Alborz Geramifard Joshua Redding Nicholas Roy and Jonathan P

UAV Cooperative Control with Stochastic Risk Models Alborz Geramifard Joshua Redding Nicholas Roy and Jonathan P

  • giovanna-bartolotta
  • 8 Slides

How Abstract Risk and reward are fundamental con...

Nonlinear Learning using Local Coordinate Coding Kai Yu NEC Laboratories America kyusv

Nonlinear Learning using Local Coordinate Coding Kai Yu NEC Laboratories America kyusv

  • test
  • 9 Slides

neclabscom Tong Zhang Rutgers University tzhangsta...

Journal of Machine Learning Research Submi tted Revised Published ErrorCorrecting Ouput Codes Library Sergio Escalera SERGIO MAIA UB ES Oriol Pujol ORIOL MAIA UB ES Petia Radeva PETIA IVANOVA U

Journal of Machine Learning Research Submi tted Revised Published ErrorCorrecting Ouput Codes Library Sergio Escalera SERGIO MAIA UB ES Oriol Pujol ORIOL MAIA UB ES Petia Radeva PETIA IVANOVA U

  • pamella-moone
  • 4 Slides

The ECOC framework is a powerful tool to deal wit...

An Introduction to Error Correction Models Robin Best Oxford Spring School for Quantitative Methods in Social Research An Introduction to ECMs Error Correction Models ECMs are a category of mu ltipl

An Introduction to Error Correction Models Robin Best Oxford Spring School for Quantitative Methods in Social Research An Introduction to ECMs Error Correction Models ECMs are a category of mu ltipl

  • stefany-barnette
  • 16 Slides

ECMs are useful for estimating both short term an...

Coupled SemiSupervised Learning for Information Extraction Andrew Carlson Schoool of Computer Science Carnegie Mellon University Pittsburgh PA acarlsoncs

Coupled SemiSupervised Learning for Information Extraction Andrew Carlson Schoool of Computer Science Carnegie Mellon University Pittsburgh PA acarlsoncs

  • celsa-spraggs
  • 10 Slides

cmuedu Justin Betteridge Schoool of Computer Scien...

SemiCoupled Dictionary Learning with Applications to Image SuperResolution and PhotoSketch Synthesis Shenlong Wang Lei Zhang Yan Liang Quan Pan Northwestern Polytechnical University The Hong Kong Pol

SemiCoupled Dictionary Learning with Applications to Image SuperResolution and PhotoSketch Synthesis Shenlong Wang Lei Zhang Yan Liang Quan Pan Northwestern Polytechnical University The Hong Kong Pol

  • myesha-ticknor
  • 8 Slides

wanggmailcom cslzhangcomppolyueduhk liangyannwpued...

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