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31,597
Spikernels: Embedding Spiking Neurons in InnerProduct Spaces
, 2002
"... Innerproduct operators, often referred to as kernels in statistical learning, define a mapping from some input space into a feature space. The focus of this paper is the construction of biologicallymotivated kernels for cortical activities. The kernels we derive, termed Spikernels, map spike co ..."
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Innerproduct operators, often referred to as kernels in statistical learning, define a mapping from some input space into a feature space. The focus of this paper is the construction of biologicallymotivated kernels for cortical activities. The kernels we derive, termed Spikernels, map spike
Spikernels: Embedding spiking neurons in inner product spaces
 Advances in Neural Information Processing Systems 15
, 2003
"... Innerproduct operators, often referred to as kernels in statistical learning, define a mapping from some input space into a feature space. The focus of this paper is the construction of biologicallymotivated kernels for cortical activities. The kernels we derive, termed Spikernels, map spike count ..."
Abstract

Cited by 12 (3 self)
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Innerproduct operators, often referred to as kernels in statistical learning, define a mapping from some input space into a feature space. The focus of this paper is the construction of biologicallymotivated kernels for cortical activities. The kernels we derive, termed Spikernels, map spike
For Most Large Underdetermined Systems of Linear Equations the Minimal ℓ1norm Solution is also the Sparsest Solution
 Comm. Pure Appl. Math
, 2004
"... We consider linear equations y = Φα where y is a given vector in R n, Φ is a given n by m matrix with n < m ≤ An, and we wish to solve for α ∈ R m. We suppose that the columns of Φ are normalized to unit ℓ 2 norm 1 and we place uniform measure on such Φ. We prove the existence of ρ = ρ(A) so that ..."
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Cited by 560 (10 self)
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. In contrast, heuristic attempts to sparsely solve such systems – greedy algorithms and thresholding – perform poorly in this challenging setting. The techniques include the use of random proportional embeddings and almostspherical sections in Banach space theory, and deviation bounds for the eigenvalues
Imagined Communities
, 1991
"... This is a field report of a threeweek experience in Japan, centered on art education in their cultural and social contexts. Beginning with this overarching focus, the themes and patterns that structure this report were emergent, rising from the experience. Those supporting themes are: being in Japa ..."
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Cited by 802 (5 self)
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This is a field report of a threeweek experience in Japan, centered on art education in their cultural and social contexts. Beginning with this overarching focus, the themes and patterns that structure this report were emergent, rising from the experience. Those supporting themes are: being in Japan and in Mino city (setting a context); the culture of handmade Washi paper; the qualities of the Washi paper festival; craft as a way of teaching, being and learning; children and their art at school and through the festival, and the importance of ritual. This report is written in a personal narrative style as suggested in contemporary feminist and transactive ethnographic literature. Key Words：crosscultural art education, feminist, transactive ethnography, Japanese art education Report from Japan: Art,
Near Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
, 2004
"... Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear m ..."
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Cited by 1513 (20 self)
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Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear measurements do we need to recover objects from this class to within accuracy ɛ? This paper shows that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal f ∈ F decay like a powerlaw (or if the coefficient sequence of f in a fixed basis decays like a powerlaw), then it is possible to reconstruct f to within very high accuracy from a small number of random measurements. typical result is as follows: we rearrange the entries of f (or its coefficients in a fixed basis) in decreasing order of magnitude f  (1) ≥ f  (2) ≥... ≥ f  (N), and define the weakℓp ball as the class F of those elements whose entries obey the power decay law f  (n) ≤ C · n −1/p. We take measurements 〈f, Xk〉, k = 1,..., K, where the Xk are Ndimensional Gaussian
Wireless Communications
, 2005
"... Copyright c ○ 2005 by Cambridge University Press. This material is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University ..."
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Cited by 1129 (32 self)
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Copyright c ○ 2005 by Cambridge University Press. This material is in copyright. Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University
Results 1  10
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