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» GaP: a factor model for discrete data
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BMCBI
2008
172views more  BMCBI 2008»
13 years 4 months ago
Compo: composite motif discovery using discrete models
Background: Computational discovery of motifs in biomolecular sequences is an established field, with applications both in the discovery of functional sites in proteins and regula...
Geir Kjetil Sandve, Osman Abul, Finn Drablø...
ICML
2004
IEEE
14 years 5 months ago
The multiple multiplicative factor model for collaborative filtering
We describe a class of causal, discrete latent variable models called Multiple Multiplicative Factor models (MMFs). A data vector is represented in the latent space as a vector of...
Benjamin M. Marlin, Richard S. Zemel
JMLR
2006
120views more  JMLR 2006»
13 years 4 months ago
Learning Parts-Based Representations of Data
Many perceptual models and theories hinge on treating objects as a collection of constituent parts. When applying these approaches to data, a fundamental problem arises: how can w...
David A. Ross, Richard S. Zemel
IPPS
2000
IEEE
13 years 9 months ago
Dynamic Data Layouts for Cache-Conscious Factorization of DFT
Effective utilization of cache memories is a key factor in achieving high performance in computing the Discrete Fourier Transform (DFT). Most optimizationtechniques for computing ...
Neungsoo Park, Dongsoo Kang, Kiran Bondalapati, Vi...
KDD
2010
ACM
435views Data Mining» more  KDD 2010»
13 years 8 months ago
Topic models with power-law using Pitman-Yor process
One of the important approaches for Knowledge discovery and Data mining is to estimate unobserved variables because latent variables can indicate hidden and specific properties o...
Issei Sato, Hiroshi Nakagawa