7:30
|
Opening remarks and overview
|
Trevor Darrell
MIT Computer Science and Artificial Intelligence Lab |
  7:40
|
Locality-Sensitive Hashing Scheme Based on p-Stable Distributions
|
Mayur Datar
Stanford University |
  8:00
|
Adaptive Mean Shift Based Clustering in High Dimensions
|
Ilan Shimshoni
Technion |
  8:20
|
Approximate Nearest Neighbor Regression in Very High Dimensions
|
Stefan Schaal
University of Southern California |
  8:40
|
Q & A
|
|
  8:50
|
Break
|
|
  9:00
|
Object recognition using locality sensitive hashing of shapemes
|
Jitendra Malik
University of California, Berkley |
  9:20
|
Embedding Earth-Mover Distance into Normed Spaces
|
Piotr Indyk
MIT Computer Science and Artificial Intelligence Lab |
  9:40
|
Moderated discussion
|
|
  10:20
|
'Spotlight' presentations
|
|
  4:00
|
On the Risk of the Approximate Nearest
Neighbor Classifier
|
John Fisher
MIT |
  4:20
|
Computing K-NN classifications without finding the K-NN
|
Andrew Moore
Carnegie Mellon University |
  4:40
|
Nearest Neighbors in Metric Spaces
|
Ken Clarkson
Bell Labs |
  5:00
|
Moderated discussion
|
|
  5:25
|
Break
|
|
  5:30
|
Fast Example-Based Estimation with Parameter-Sensitive Hashing
|
Greg Shakhnarovich
MIT Computer Science and Artificial Intelligence Lab |
  5:50
|
Limitations on the performance and approximation of high dimensional
nearest neighbor searches
|
Jonathan Goldstein
Microsoft Research |
  6:10
|
Approximate Nearest Neighbor Retrieval Using Euclidean Embeddings
|
Vassilis Athitsos
Boston University |
  6:30
|
Moderated discussion
|
|
  6:55
|
Closing remarks
|
Trevor Darrell
MIT Computer Science and Artificial Intelligence Lab |