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ICML
2009
IEEE
14 years 11 months ago
A stochastic memoizer for sequence data
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
Frank Wood, Cédric Archambeau, Jan Gasthaus...
FGR
2004
IEEE
167views Biometrics» more  FGR 2004»
14 years 1 months ago
Adaptive Learning of an Accurate Skin-Color Model
Due to variations of lighting conditions, camera hardware settings, and the range of skin coloration among human beings, a pre-defined skin-color model cannot accurately capture t...
Qiang Zhu, Kwang-Ting Cheng, Ching-Tung Wu, Yi-Leh...
DAGSTUHL
2007
13 years 11 months ago
Learning Probabilistic Relational Dynamics for Multiple Tasks
The ways in which an agent’s actions affect the world can often be modeled compactly using a set of relational probabilistic planning rules. This paper addresses the problem of ...
Ashwin Deshpande, Brian Milch, Luke S. Zettlemoyer...
ICASSP
2011
IEEE
13 years 1 months ago
Learning vocal tract variables with multi-task kernels
The problem of acoustic-to-articulatory speech inversion continues to be a challenging research problem which significantly impacts automatic speech recognition robustness and ac...
Hachem Kadri, Emmanuel Duflos, Philippe Preux
SSPR
2010
Springer
13 years 8 months ago
Information Theoretical Kernels for Generative Embeddings Based on Hidden Markov Models
Many approaches to learning classifiers for structured objects (e.g., shapes) use generative models in a Bayesian framework. However, state-of-the-art classifiers for vectorial d...
André F. T. Martins, Manuele Bicego, Vittor...