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CVPR
2010
IEEE
14 years 6 months ago
Increasing depth resolution of Electron Microscopy of Neural circuits using Sparse Tomographic reconstruction
Future progress in neuroscience hinges on reconstruction of neuronal circuits to the level of individual synapses. Because of the specifics of neuronal architecture, imaging must ...
Ashok Veeraraghavan, Alex Genkin, Shiv Vitaladevun...
BC
2006
80views more  BC 2006»
13 years 10 months ago
Sparse Spatial Sampling for the Computation of Motion in Multiple Stages
The avian retino-tecto-rotundal pathway plays a central role in motion analysis and features complex connectivity. Yet, the relation between the pathway's structural arrangeme...
Alireza S. Mahani, Reza Khanbabaie, Harald Luksch,...
JMLR
2010
202views more  JMLR 2010»
13 years 4 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
SIAMJO
2011
13 years 5 months ago
Recovering Low-Rank and Sparse Components of Matrices from Incomplete and Noisy Observations
Many applications arising in a variety of fields can be well illustrated by the task of recovering the low-rank and sparse components of a given matrix. Recently, it is discovered...
Min Tao, Xiaoming Yuan
ATAL
2007
Springer
14 years 4 months ago
Sharing experiences to learn user characteristics in dynamic environments with sparse data
This paper investigates the problem of estimating the value of probabilistic parameters needed for decision making in environments in which an agent, operating within a multi-agen...
David Sarne, Barbara J. Grosz