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ICML
2007
IEEE
16 years 8 months ago
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process
A new hierarchical nonparametric Bayesian model is proposed for the problem of multitask learning (MTL) with sequential data. Sequential data are typically modeled with a hidden M...
Kai Ni, Lawrence Carin, David B. Dunson
EDBT
2009
ACM
118views Database» more  EDBT 2009»
16 years 2 months ago
Flexible query answering on graph-modeled data
The largeness and the heterogeneity of most graph-modeled datasets in several database application areas make the query process a real challenge because of the lack of a complete ...
Federica Mandreoli, Riccardo Martoglia, Giorgio Vi...
UAI
2003
15 years 8 months ago
Bayesian Hierarchical Mixtures of Experts
The Hierarchical Mixture of Experts (HME) is a well-known tree-structured model for regression and classification, based on soft probabilistic splits of the input space. In its o...
Christopher M. Bishop, Markus Svensén
NN
2006
Springer
121views Neural Networks» more  NN 2006»
15 years 7 months ago
Mirror neurons and imitation: A computationally guided review
Neurophysiology reveals the properties of individual mirror neurons in the macaque while brain imaging reveals the presence of `mirror systems' (not individual neurons) in th...
Erhan Oztop, Mitsuo Kawato, Michael A. Arbib
BMVC
2010
15 years 5 months ago
Probabilistic Latent Sequential Motifs: Discovering Temporal Activity Patterns in Video Scenes
This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns from documents given as word-time occurrences. In this model, docum...
Jagannadan Varadarajan, Rémi Emonet, Jean-M...