<--- Back to Details
First PageDocument Content
Algorithms / Engineering / Academia / Applied mathematics / Operations research / Mathematical logic / Theoretical computer science / Human-based computation / Divide and conquer algorithms / Crowdsourcing / Problem solving / Computer science
Date: 2011-03-24 21:28:49
Algorithms
Engineering
Academia
Applied mathematics
Operations research
Mathematical logic
Theoretical computer science
Human-based computation
Divide and conquer algorithms
Crowdsourcing
Problem solving
Computer science

Crowdsourcing General Computation Haoqi Zhang∗ , Eric Horvitz† , Rob C. Miller‡ , and David C. Parkes∗ ∗ Harvard SEAS Cambridge, MA 02138, USA {hq, parkes}@eecs.harvard.edu

Add to Reading List

Source URL: crowdresearch.org

Download Document from Source Website

File Size: 725,17 KB

Share Document on Facebook

Similar Documents

A	
  Family	
  of	
  Provably	
  Correct	
  Algorithms	
   for	
  Exact	
  Triangle	
  Coun;ng	
  	
   	
   Ma=hew	
  Lee,	
  Tze	
  Meng	
  Low	
   Correctness	
  2017	
   	
  

A  Family  of  Provably  Correct  Algorithms   for  Exact  Triangle  Coun;ng       Ma=hew  Lee,  Tze  Meng  Low   Correctness  2017    

DocID: 1xVUG - View Document

Theoretical Computer Science–40  www.elsevier.com/locate/tcs Presorting algorithms: An average-case point of view

Theoretical Computer Science–40 www.elsevier.com/locate/tcs Presorting algorithms: An average-case point of view

DocID: 1xVR3 - View Document

Formal Correctness of Comparison Algorithms between Binary64 and Decimal64 Floating-point Numbers Arthur Blot ENS Lyon, France  NSV, July 22-23, 2017

Formal Correctness of Comparison Algorithms between Binary64 and Decimal64 Floating-point Numbers Arthur Blot ENS Lyon, France NSV, July 22-23, 2017

DocID: 1xVvl - View Document

Theory and Techniques for Synthesizing a Family of Graph Algorithms Srinivas Nedunuri William R. Cook

Theory and Techniques for Synthesizing a Family of Graph Algorithms Srinivas Nedunuri William R. Cook

DocID: 1xVkB - View Document

Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms Ari Juels Department of Computer Science

Stochastic Hillclimbing as a Baseline Method for Evaluating Genetic Algorithms Ari Juels Department of Computer Science

DocID: 1xVfc - View Document