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CVPR
2009
IEEE
17 years 1 months ago
Unsupervised Learning of Hierarchical Spatial Structures In Images
The visual world demonstrates organized spatial patterns, among objects or regions in a scene, object-parts in an object, and low-level features in object-parts. These classes o...
Devi Parikh (Carnegie Mellon University), C. Lawre...
VISUALIZATION
1996
IEEE
15 years 10 months ago
Hierarchical and Parallelizable Direct Volume Rendering for Irregular and Multiple Grids
A general volume rendering technique is described that efficiently produces images of excellent quality from data defined over irregular grids having a wide variety of formats. Re...
Jane Wilhelms, Allen Van Gelder, Paul Tarantino, J...
KDD
2003
ACM
191views Data Mining» more  KDD 2003»
16 years 7 months ago
Assessment and pruning of hierarchical model based clustering
The goal of clustering is to identify distinct groups in a dataset. The basic idea of model-based clustering is to approximate the data density by a mixture model, typically a mix...
Jeremy Tantrum, Alejandro Murua, Werner Stuetzle
RECOMB
2007
Springer
16 years 6 months ago
Minimizing and Learning Energy Functions for Side-Chain Prediction
Abstract. Side-chain prediction is an important subproblem of the general protein folding problem. Despite much progress in side-chain prediction, performance is far from satisfact...
Chen Yanover, Ora Schueler-Furman, Yair Weiss
CSDA
2008
80views more  CSDA 2008»
15 years 6 months ago
Variational Bayesian functional PCA
A Bayesian approach to analyze the modes of variation in a set of curves is suggested. It is based on a generative model thus allowing for noisy and sparse observations of curves....
Angelika van der Linde