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JMLR
2010
160views more  JMLR 2010»
15 years 1 months ago
Neural conditional random fields
We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Mar...
Trinh Minh Tri Do, Thierry Artières
BMCBI
2007
104views more  BMCBI 2007»
15 years 6 months ago
A response to Yu et al. "A forward-backward fragment assembling algorithm for the identification of genomic amplification and de
Background: Yu et al. (BMC Bioinformatics 2007,8: 145+) have recently compared the performance of several methods for the detection of genomic amplification and deletion breakpoin...
Oscar M. Rueda, Ramón Díaz-Uriarte
PE
2010
Springer
102views Optimization» more  PE 2010»
15 years 5 months ago
Extracting state-based performance metrics using asynchronous iterative techniques
Solution of large sparse linear fixed-point problems lies at the heart of many important performance analysis calculations. These calculations include steady-state, transient and...
Douglas V. de Jager, Jeremy T. Bradley
ICIP
2008
IEEE
16 years 8 months ago
Motion blur free HDR image acquisition using multiple exposures
The high dynamic range image (HDRI) acquisition method based on Markov random field model is proposed. By combining multiple exposure images shot with different shutter speed, we ...
Takao Jinno, Masahiro Okuda
ICIP
2000
IEEE
16 years 8 months ago
Definition of a Spatial Entropy and its Use for Texture Discrimination
This paper presents a new definition of a spatial entropy mainly based on the Markov Random Field (MRF) properties. Starting with the study of the entropy proposed in [1] for the ...
Florence Tupin, Henri Maître, Marc Sigelle