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NIPS
2007
13 years 9 months ago
Near-Maximum Entropy Models for Binary Neural Representations of Natural Images
Maximum entropy analysis of binary variables provides an elegant way for studying the role of pairwise correlations in neural populations. Unfortunately, these approaches suffer f...
Matthias Bethge, Philipp Berens
DAGM
2011
Springer
12 years 7 months ago
Putting MAP Back on the Map
Conditional Random Fields (CRFs) are popular models in computer vision for solving labeling problems such as image denoising. This paper tackles the rarely addressed but important ...
Patrick Pletscher, Sebastian Nowozin, Pushmeet Koh...
ICRA
2009
IEEE
145views Robotics» more  ICRA 2009»
14 years 2 months ago
Distributed maximum a posteriori estimation for multi-robot cooperative localization
— This paper presents a distributed Maximum A Posteriori (MAP) estimator for multi-robot Cooperative Localization (CL). As opposed to centralized MAP-based CL, the proposed algor...
Esha D. Nerurkar, Stergios I. Roumeliotis, Agostin...
JMLR
2008
209views more  JMLR 2008»
13 years 7 months ago
Bayesian Inference and Optimal Design for the Sparse Linear Model
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
Matthias W. Seeger
IPMI
2009
Springer
14 years 8 months ago
Bayesian Registration via Local Image Regions: Information, Selection and Marginalization
We propose a novel Bayesian registration formulation in which image location is represented as a latent random variable. Location is marginalized to determine the maximum a priori ...
Matthew Toews, William M. Wells III