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» Learning the Structure of Linear Latent Variable Models
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NIPS
1993
15 years 7 months ago
The Power of Amnesia
We propose a learning algorithm for a variable memory length Markov process. Human communication, whether given as text, handwriting, or speech, has multi characteristic time scal...
Dana Ron, Yoram Singer, Naftali Tishby
EMNLP
2010
15 years 4 months ago
Turbo Parsers: Dependency Parsing by Approximate Variational Inference
We present a unified view of two state-of-theart non-projective dependency parsers, both approximate: the loopy belief propagation parser of Smith and Eisner (2008) and the relaxe...
André F. T. Martins, Noah A. Smith, Eric P....
ECSQARU
2001
Springer
15 years 10 months ago
Caveats for Causal Reasoning with Equilibrium Models
In this paper we examine the ability to perform causal reasoning with equilibrium models. We explicate a postulate, which we term the Manipulation Postulate, that is required in o...
Denver Dash, Marek J. Druzdzel
BMCBI
2010
229views more  BMCBI 2010»
15 years 6 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck
IJNS
2010
106views more  IJNS 2010»
15 years 4 months ago
Cascade Process Modeling with Mechanism-Based Hierarchical Neural Networks
Abstract: Cascade process, such as wastewater treatment plant, includes many nonlinear subsystems and many variables. When the number of sub-systems is big, the input-output relati...
Qiumei Cong, Wen Yu, Tianyou Chai