'Sparse' diaporamas de présentation

Data Structures and Algorithms Graphs

Data Structures and Algorithms Graphs. Content. Motivation Graph Directed Graph Undirected Graph Representation Implementation Algorithms Graph Traversal Shortest Path Minimum Cost Spanning Trees Critical Path Analysis. Motivation ... Graphs are everywhere. Directed Graph.

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Linguistic Regularities in Sparse and Explicit Word Representations

Linguistic Regularities in Sparse and Explicit Word Representations. Omer Levy Yoav Goldberg Bar- Ilan University Israel. Papers in ACL 2014*. * Sampling error: +/- 100%. Neural Embeddings. Dense vectors Each dimension is a latent feature Common software package: word2vec

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How To Do Multivariate Pattern Analysis

How To Do Multivariate Pattern Analysis. What is MVPA?. Animations from Meyer & Kaplan (in press), Journal of Visualized Experiments. Not significant. Average. Average. V 1. V 2. V 3. V 4. V 5. V 6. V 1. V 2. V 3. V 4. V 5. V 6. Univariate vs. multivariate analysis of

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Meta-Simulation Design and Analysis for Large Scale Networks

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Unsupervised Feature Selection for Multi-Cluster Data Deng Cai et al, KDD 2010

Unsupervised Feature Selection for Multi-Cluster Data Deng Cai et al, KDD 2010. Presenter: Yunchao Gong Dept. Computer Science, UNC Chapel Hill. Introduction . The problem. Feature selection for high dimensional data Assume high dimensional n*d data matrix X

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Recovery of Clustered Sparse Signals from Compressive Measurements

Recovery of Clustered Sparse Signals from Compressive Measurements. Chinmay Hegde. Richard Baraniuk. Volkan Cevher volkan@rice.edu. Piotr Indyk. The Digital Universe. Size: ~300 billion gigabytes generated in 2007 digital bits > stars in the universe

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IP Multicasting: Multicast Configuration and Verification

IP Multicasting: Multicast Configuration and Verification. BSCI Module 7 Lesson 4. Objectives. Enable PIM Sparse Mode and Sparse-Dense Mode on a router interface. Verify the multicast routing table. Determine if PIM neighbors are correctly configured.

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Hearing loss and sparse coding

Hearing loss and sparse coding. Stefan Bleeck, Institute of Sound and Vibration Research, Hearing and Balance Centre University of Southampton . Question:. Can sparse coding help to overcome problems caused by hearing loss? overview of the hearing process

548 views • 33 slides

The Zambezi River Valley and surrounding areas, July, 2011.

Seasonal Biomass Burning in Africa and its effects on air quality: a study of AOD in 2008-2010 with some comments on long-range transport. The Zambezi River Valley and surrounding areas, July, 2011. MODIS above the Democratic Republic of Congo, May 16, 2002. w ith Ben Sumlin.

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STAR-P: High Productivity Parallel Computing

STAR-P: High Productivity Parallel Computing. David Cheng, Ron Choy, Alan Edelman Massachusetts Institute of Technology John R. Gilbert and Viral Shah University of California at Santa Barbara. Graph Algorithms and Sparse Matrix Land. Birth of Interactive Supercomputing.

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Abraham Gallas for the LHCb VELO Upgrade

The LHCb Upgrade from 1 to 40 MHz readout. The 20th Anniversary International Workshop on Vertex Detectors 19-24 June 2011, Rust , Austria. Abraham Gallas for the LHCb VELO Upgrade. Outline:. The LHCb upgrade: scenario & strategy The Vertex detector upgrade: Environment & plan

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Symmetric-pattern multifrontal factorization

4. 1. G(A) . 7. 1. 2. 3. 4. 5. 6. 7. 8. 9. 6. 3. 8. 1. 2. 2. 5. 9. 3. 4. 5. 6. 9. T(A) . 7. 8. 8. 9. 7. A . 6. 3. 4. 1. 2. 5. Symmetric-pattern multifrontal factorization. 4. 1. G(A) . 7. 6. 3. 8. 2. 5. 9. 9. T(A) . 8. 7. 6. 3. 4. 1. 2. 5.

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Lecture 4 Sparse Factorization: Data-flow Organization

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MLD2P4: a package of parallel algebraic multilevel Preconditioners

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Richard Baraniuk Rice University dsp.rice/cs

Compressive Signal Processing. Richard Baraniuk Rice University dsp.rice.edu/cs. Compressive Sensing (CS). When data is sparse/compressible, can directly acquire a condensed representation with no/little information loss Random projection will work. sparse signal. measurements.

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Sparse Systems and Iterative Methods

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Computational Cognitive Neuroscience Lab

Computational Cognitive Neuroscience Lab. Today: Second session. Computational Cognitive Neuroscience Lab. Today: Homework is due Friday, Feb 10 This homework has more projects than the last, but fewer questions per project. Bias Weights. Why do we have them?

247 views • 8 slides

Sparse Matrix Factorizations for Hyperspectral Unmixing

Sparse Matrix Factorizations for Hyperspectral Unmixing. John Wright Visual Computing Group Microsoft Research Asia. Sept . 30 , 2010. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A A A A A A A A A A. Problem setting.

207 views • 7 slides