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ICANN
2010
Springer
13 years 8 months ago
Deep Bottleneck Classifiers in Supervised Dimension Reduction
Deep autoencoder networks have successfully been applied in unsupervised dimension reduction. The autoencoder has a "bottleneck" middle layer of only a few hidden units, ...
Elina Parviainen
SIGMETRICS
1992
ACM
128views Hardware» more  SIGMETRICS 1992»
14 years 19 days ago
MemSpy: Analyzing Memory System Bottlenecks in Programs
To cope with the increasing difference between processor and main memory speeds, modern computer systems use deep memory hierarchies. In the presence of such hierarchies, the perf...
Margaret Martonosi, Anoop Gupta, Thomas E. Anderso...
SIGIR
2002
ACM
13 years 8 months ago
Unsupervised document classification using sequential information maximization
We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential ...
Noam Slonim, Nir Friedman, Naftali Tishby
PE
1998
Springer
158views Optimization» more  PE 1998»
13 years 8 months ago
Asymptotic Approximations and Bottleneck Analysis in Product Form Queueing Networks with Large Populations
Asymptotic approximations are constructed for the performance measures of product form queueing networks consisting of single server, fixed rate nodes with large populations. The...
Charles Knessl, Charles Tier
EURODAC
1995
IEEE
164views VHDL» more  EURODAC 1995»
14 years 3 days ago
Bottleneck removal algorithm for dynamic compaction and test cycles reduction
: We present a new, dynamic algorithm for test sequence compaction and test cycle reduction for combinationaland sequential circuits. Several dynamic algorithms for compaction in c...
Srimat T. Chakradhar, Anand Raghunathan