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CVPR
2012
IEEE
6 years 10 months ago
A tiered move-making algorithm for general pairwise MRFs
A large number of problems in computer vision can be modeled as energy minimization problems in a markov random field (MRF) framework. Many methods have been developed over the y...
Vibhav Vineet, Jonathan Warrell, Philip H. S. Torr
CVPR
2012
IEEE
6 years 10 months ago
From Pictorial Structures to deformable structures
Pictorial Structures (PS) define a probabilistic model of 2D articulated objects in images. Typical PS models assume an object can be represented by a set of rigid parts connecte...
Silvia Zuffi, Oren Freifeld, Michael J. Black
CORR
2012
Springer
170views Education» more  CORR 2012»
7 years 4 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
CORR
2012
Springer
281views Education» more  CORR 2012»
7 years 4 months ago
Belief Propagation by Message Passing in Junction Trees: Computing Each Message Faster Using GPU Parallelization
Compiling Bayesian networks (BNs) to junction trees and performing belief propagation over them is among the most prominent approaches to computing posteriors in BNs. However, bel...
Lu Zheng, Ole J. Mengshoel, Jike Chong
EMNLP
2011
7 years 8 months ago
Training a Log-Linear Parser with Loss Functions via Softmax-Margin
Log-linear parsing models are often trained by optimizing likelihood, but we would prefer to optimise for a task-specific metric like Fmeasure. Softmax-margin is a convex objecti...
Michael Auli, Adam Lopez
AAAI
2011
7 years 8 months ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
AAAI
2011
7 years 8 months ago
Dual Decomposition for Marginal Inference
We present a dual decomposition approach to the treereweighted belief propagation objective. Each tree in the tree-reweighted bound yields one subproblem, which can be solved with...
Justin Domke
INFOCOM
2011
IEEE
7 years 11 months ago
SigSag: Iterative detection through soft message-passing
—The multiple-access framework of ZigZag decoding [1] is a useful technique for combating interference via multiple repeated transmissions, and is known to be compatible with dis...
Arash Saber Tehrani, Alexandros G. Dimakis, Michae...
CHI
2011
ACM
7 years 12 months ago
Apolo: making sense of large network data by combining rich user interaction and machine learning
Extracting useful knowledge from large network datasets has become a fundamental challenge in many domains, from scientific literature to social networks and the web. We introduc...
Duen Horng Chau, Aniket Kittur, Jason I. Hong, Chr...
AI
2011
Springer
7 years 12 months ago
Parallelizing a Convergent Approximate Inference Method
Probabilistic inference in graphical models is a prevalent task in statistics and artificial intelligence. The ability to perform this inference task efficiently is critical in l...
Ming Su, Elizabeth Thompson
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