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119
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NIPS
2004
15 years 3 months ago
Learning Efficient Auditory Codes Using Spikes Predicts Cochlear Filters
The representation of acoustic signals at the cochlear nerve must serve a wide range of auditory tasks that require exquisite sensitivity in both time and frequency. Lewicki (2002...
Evan C. Smith, Michael S. Lewicki
149
Voted
TIP
2010
155views more  TIP 2010»
15 years 17 days ago
Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval
—In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels ...
Xiaofei He
171
Voted
AAAI
2011
14 years 2 months ago
An Online Spectral Learning Algorithm for Partially Observable Nonlinear Dynamical Systems
Recently, a number of researchers have proposed spectral algorithms for learning models of dynamical systems—for example, Hidden Markov Models (HMMs), Partially Observable Marko...
Byron Boots, Geoffrey J. Gordon
93
Voted
IJON
2007
117views more  IJON 2007»
15 years 2 months ago
Learning sensory representations with intrinsic plasticity
Intrinsic plasticity (IP) refers to a neuron’s ability to regulate its firing activity by adapting its intrinsic excitability. Previously, we showed that model neurons combinin...
Nicholas Butko, Jochen Triesch
147
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SCALESPACE
2007
Springer
15 years 8 months ago
Non-negative Sparse Modeling of Textures
This paper presents a statistical model for textures that uses a non-negative decomposition on a set of local atoms learned from an exemplar. This model is described by the varianc...
Gabriel Peyré