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153
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NIPS
2004
15 years 5 months ago
Maximising Sensitivity in a Spiking Network
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Anthony J. Bell, Lucas C. Parra
127
Voted
SDM
2007
SIAM
198views Data Mining» more  SDM 2007»
15 years 5 months ago
Learning from Time-Changing Data with Adaptive Windowing
We present a new approach for dealing with distribution change and concept drift when learning from data sequences that may vary with time. We use sliding windows whose size, inst...
Albert Bifet, Ricard Gavaldà
159
Voted
ICS
2007
Tsinghua U.
15 years 9 months ago
Adaptive Strassen's matrix multiplication
Strassen’s matrix multiplication (MM) has benefits with respect to any (highly tuned) implementations of MM because Strassen’s reduces the total number of operations. Strasse...
Paolo D'Alberto, Alexandru Nicolau
114
Voted
APPINF
2003
15 years 5 months ago
Debugging Distributed Computations by Reverse Search
We develop a memory-efficient off-line algorithm for the enumeration of global states of a distributed computation. The algorithm allows the parameterization of its memory requir...
Artur Andrzejak, Komei Fukuda
156
Voted
EDOC
2011
IEEE
14 years 3 months ago
UML Metamodel-based Workflow Modeling and Execution
—In this paper, we present a UML metamodel-based approach for creating and executing workflow models. The modeling language is introduced through its abstract syntax, and an eval...
Jens Brüning, Martin Gogolla