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» The Power of Temporal Proofs
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FLAIRS
2008
15 years 8 months ago
Learning Dynamic Naive Bayesian Classifiers
Hidden Markov models are a powerful technique to model and classify temporal sequences, such as in speech and gesture recognition. However, defining these models is still an art: ...
Miriam Martínez, Luis Enrique Sucar
NSDI
2007
15 years 8 months ago
Friday: Global Comprehension for Distributed Replay
Debugging and profiling large-scale distributed applications is a daunting task. We present Friday, a system for debugging distributed applications that combines deterministic re...
Dennis Geels, Gautam Altekar, Petros Maniatis, Tim...
NIPS
2008
15 years 7 months ago
Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks
Offline handwriting recognition--the transcription of images of handwritten text--is an interesting task, in that it combines computer vision with sequence learning. In most syste...
Alex Graves, Jürgen Schmidhuber
NIPS
2007
15 years 7 months ago
Second Order Bilinear Discriminant Analysis for single trial EEG analysis
Traditional analysis methods for single-trial classification of electroencephalography (EEG) focus on two types of paradigms: phase locked methods, in which the amplitude of the ...
Christoforos Christoforou, Paul Sajda, Lucas C. Pa...
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NIPS
2007
15 years 7 months ago
An online Hebbian learning rule that performs Independent Component Analysis
Independent component analysis (ICA) is a powerful method to decouple signals. Most of the algorithms performing ICA do not consider the temporal correlations of the signal, but o...
Claudia Clopath, André Longtin, Wulfram Ger...