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AAAI
2012
9 years 7 months ago
Advances in Lifted Importance Sampling
We consider lifted importance sampling (LIS), a previously proposed approximate inference algorithm for statistical relational learning (SRL) models. LIS achieves substantial vari...
Vibhav Gogate, Abhay Kumar Jha, Deepak Venugopal
JMLR
2012
9 years 7 months ago
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation
This paper considers additive factorial hidden Markov models, an extension to HMMs where the state factors into multiple independent chains, and the output is an additive function...
J. Zico Kolter, Tommi Jaakkola
CORR
2012
Springer
170views Education» more  CORR 2012»
10 years 1 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
ESOP
2011
Springer
10 years 9 months ago
Measure Transformer Semantics for Bayesian Machine Learning
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
Johannes Borgström, Andrew D. Gordon, Michael...
CISS
2010
IEEE
10 years 9 months ago
Turbo reconstruction of structured sparse signals
—This paper considers the reconstruction of structured-sparse signals from noisy linear observations. In particular, the support of the signal coefficients is parameterized by h...
Philip Schniter
JMLR
2010
192views more  JMLR 2010»
11 years 6 days ago
Efficient Learning of Deep Boltzmann Machines
We present a new approximate inference algorithm for Deep Boltzmann Machines (DBM's), a generative model with many layers of hidden variables. The algorithm learns a separate...
Ruslan Salakhutdinov, Hugo Larochelle
JMLR
2010
148views more  JMLR 2010»
11 years 7 days ago
Approximate Inference on Planar Graphs using Loop Calculus and Belief Propagation
We introduce novel results for approximate inference on planar graphical models using the loop calculus framework. The loop calculus (Chertkov and Chernyak, 2006b) allows to expre...
Vicenç Gómez, Hilbert J. Kappen, Mic...
JMLR
2010
141views more  JMLR 2010»
11 years 7 days ago
FastInf: An Efficient Approximate Inference Library
The FastInf C++ library is designed to perform memory and time efficient approximate inference in large-scale discrete undirected graphical models. The focus of the library is pro...
Ariel Jaimovich, Ofer Meshi, Ian McGraw, Gal Elida...
CVPR
2011
IEEE
11 years 1 months ago
Learning Message-Passing Inference Machines for Structured Prediction
Nearly every structured prediction problem in computer vision requires approximate inference due to large and complex dependencies among output labels. While graphical models prov...
Stephane Ross, Daniel Munoz, J. Andrew Bagnell
PAMI
2008
145views more  PAMI 2008»
11 years 5 months ago
Latent-Space Variational Bayes
Variational Bayesian Expectation-Maximization (VBEM), an approximate inference method for probabilistic models based on factorizing over latent variables and model parameters, has ...
JaeMo Sung, Zoubin Ghahramani, Sung Yang Bang
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