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JCNS
2010
104views more  JCNS 2010»
15 years 4 months ago
A new look at state-space models for neural data
State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely o...
Liam Paninski, Yashar Ahmadian, Daniel Gil Ferreir...
ICIP
2010
IEEE
15 years 4 months ago
Restoration of images and 3D data to higher resolution by deconvolution with sparsity regularization
Image convolution is conventionally approximated by the LTI discrete model. It is well recognized that the higher the sampling rate, the better is the approximation. However somet...
Yingsong Zhang, Nick G. Kingsbury
SI3D
2006
ACM
16 years 9 days ago
View-dependent precomputed light transport using nonlinear Gaussian function approximations
We propose a real-time method for rendering rigid objects with complex view-dependent effects under distant all-frequency lighting. Existing precomputed light transport approaches...
Paul Green, Jan Kautz, Wojciech Matusik, Fré...
FOCS
2005
IEEE
15 years 12 months ago
How to Pay, Come What May: Approximation Algorithms for Demand-Robust Covering Problems
Robust optimization has traditionally focused on uncertainty in data and costs in optimization problems to formulate models whose solutions will be optimal in the worstcase among ...
Kedar Dhamdhere, Vineet Goyal, R. Ravi, Mohit Sing...
ESANN
2003
15 years 7 months ago
Approximately unbiased estimation of conditional variance in heteroscedastic kernel ridge regression
In this paper we extend a form of kernel ridge regression for data characterised by a heteroscedastic noise process (introduced in Foxall et al. [1]) in order to provide approxima...
Gavin C. Cawley, Nicola L. C. Talbot, Robert J. Fo...