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ACL
2012
12 years 2 days ago
Historical Analysis of Legal Opinions with a Sparse Mixed-Effects Latent Variable Model
We propose a latent variable model to enhance historical analysis of large corpora. This work extends prior work in topic modelling by incorporating metadata, and the interactions...
William Yang Wang, Elijah Mayfield, Suresh Naidu, ...
ICIAP
2007
ACM
14 years 9 months ago
Sparseness Achievement in Hidden Markov Models
In this paper, a novel learning algorithm for Hidden Markov Models (HMMs) has been devised. The key issue is the achievement of a sparse model, i.e., a model in which all irreleva...
Manuele Bicego, Marco Cristani, Vittorio Murino
SIAMIS
2011
13 years 4 months ago
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
Matthias W. Seeger, Hannes Nickisch
ICIP
2004
IEEE
14 years 11 months ago
Sparse representation of images with hybrid linear models
We propose a mixture of multiple linear models, also known as hybrid linear model, for a sparse representation of an image. This is a generalization of the conventional KarhunenLo...
Kun Huang, Allen Y. Yang, Yi Ma
IJCNN
2006
IEEE
14 years 3 months ago
Studies on Sparse Array Cortical Modeling and Memory Cognition Duality
— In this paper we have suggested a sparse three dimensional array model for the brain. Entries of the array are synaptic weights as functions of time. This is a typical four dim...
Kausik Kumar Majumdar, Robert Kozma