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NIPS
2001
13 years 9 months ago
Speech Recognition with Missing Data using Recurrent Neural Nets
In the `missing data' approach to improving the robustness of automatic speech recognition to added noise, an initial process identifies spectraltemporal regions which are do...
S. Parveen, P. Green
IJAR
2006
89views more  IJAR 2006»
13 years 8 months ago
Learning probabilistic decision graphs
Probabilistic decision graphs (PDGs) are a representation language for probability distributions based on binary decision diagrams. PDGs can encode (context-specific) independence...
Manfred Jaeger, Jens D. Nielsen, Tomi Silander
CVPR
2008
IEEE
14 years 10 months ago
Learning Bayesian Networks with qualitative constraints
Graphical models such as Bayesian Networks (BNs) are being increasingly applied to various computer vision problems. One bottleneck in using BN is that learning the BN model param...
Yan Tong, Qiang Ji
AUSAI
2006
Springer
13 years 11 months ago
Learning Hybrid Bayesian Networks by MML
Abstract. We use a Markov Chain Monte Carlo (MCMC) MML algorithm to learn hybrid Bayesian networks from observational data. Hybrid networks represent local structure, using conditi...
Rodney T. O'Donnell, Lloyd Allison, Kevin B. Korb
WWW
2010
ACM
14 years 3 months ago
Measurement-calibrated graph models for social network experiments
Access to realistic, complex graph datasets is critical to research on social networking systems and applications. Simulations on graph data provide critical evaluation of new sys...
Alessandra Sala, Lili Cao, Christo Wilson, Robert ...