'Update rule' presentation slideshows

Administration

Administration. Questions. Registration Hw3 is out Due on Thursday 10/6 Lecture Captioning (Extra-Credit) Look at Piazza for details Scribing lectures With pay; come talk to me/send email. Projects. Projects proposals are due on Friday 10/14/16

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Semidefinite Programming and Approximation Algorithms for NP-hard Problems: A Survey

Semidefinite Programming and Approximation Algorithms for NP-hard Problems: A Survey. Sanjeev Arora Princeton University. http://intractability.princeton.edu. NP-completeness. Thousands of problems are NP-complete (TSP, Scheduling, Circuit layout, Machine Learning,..).

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Shortest Paths (Review)

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Comp. Genomics

Comp. Genomics. Recitation 14 Exam preparation Biological networks. Exercise. A large PPI network G was generated using high throughput technologies. A smaller network H is known in a different organism.

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Monday, 10 March 2008 William H. Hsu Department of Computing and Information Sciences, KSU cis.ksu/Courses/Spring-2008/C

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PATH PLANNING

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Recurrent Networks

Recurrent Networks. A recurrent network is characterized by The connection graph of the network has cycles, i.e. the output of a neuron can influence its input There are no natural input and output nodes Initially each neuron has a given input state

499 views • 39 slides

Data-Intensive Distributed Computing

Data-Intensive Distributed Computing. CS 431/631 451/651 (Winter 2019). Part 6: Data Mining (1/4). February 28, 2019. Adam Roegiest Kira Systems. These slides are available at http://roegiest.com/bigdata-2019w/.

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Neural Networks Chapter 9

Neural Networks Chapter 9. Joost N. Kok Universiteit Leiden. Unsupervised Competitive Learning. Competitive learning Winner-take-all units Cluster/Categorize input data Feature mapping. 1. 2. 3. Unsupervised Competitive Learning. winner. output. input (n-dimensional).

367 views • 36 slides

Data-Intensive Distributed Computing

Data-Intensive Distributed Computing. CS 431/631 451/651 (Winter 2019). Part 6: Data Mining (1/4). October 29, 2019. Ali Abedi. These slides are available at https://www.student.cs.uwaterloo.ca/~cs451.

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Multiplicative weights method: A meta algorithm with applications to linear and semi-definite programming

Multiplicative weights method: A meta algorithm with applications to linear and semi-definite programming. Sanjeev Arora Princeton University.

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Deep Learning: Back To The Future

Deep Learning: Back To The Future. Hinton NIPS 2012 Talk Slide (More Or Less). What was hot in 1987 Neural networks What happened in ML since 1987 Computers got faster Larger data sets became available What is hot 25 years later Neural networks

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The Web as a graph

The Web as a graph. ????? ?????. ??. www ????????????? HIT ? Hab ? Authority ? PageRank ?? ????????????????. Hubs ? Authorities. Hubs Authorities. Hubs ? authorities. Hubs Authorities. The HITS algorithm. 1 a sampling step ???????????????????? web ??????????

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Bioinspired Computing Lecture 16

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Market-Based Resource Allocation for Utility Data Centers Byde et al. HP Labs Taewon Hwang

Market-Based Resource Allocation for Utility Data Centers Byde et al. HP Labs Taewon Hwang Introduction ? Today, organizations must adapt to their environments fast. ? IT became a key determinant of a company's overall effectiveness.

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SUBCHAPTER 21

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Bioinspired Computing Lecture 13

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N.J.A.C. 7:27-21 Possible Revisions

N.J.A.C. 7:27-21 Possible Revisions. ISG Meeting September 14, 2010. Reasons for Revision. To update rule to meet federal requirements To fix errors in the rule (major and minor issues) To make the data more useful

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N.J.A.C. 7:27-21 Possible Revisions

N.J.A.C. 7:27-21 Possible Revisions. ISG Meeting September 14, 2010. Reasons for Revision. To update rule to meet federal requirements To fix errors in the rule (major and minor issues) To make the data more useful

172 views • 9 slides

N.J.A.C. 7:27-21 Possible Revisions

N.J.A.C. 7:27-21 Possible Revisions. ISG Meeting September 14, 2010. Reasons for Revision. To update rule to meet federal requirements To fix errors in the rule (major and minor issues) To make the data more useful

92 views • 9 slides