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CVPR
2009
IEEE
16 years 6 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 3 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»
15 years 12 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
15 years 11 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»
14 years 11 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