Results 251 to 260 of about 649,643 (307)

Conditioned Simulations of Random Velocity Fields

Mathematical Geosciences, 2008
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Geraets, David, Lajaunie, Christian, Ruffo, P.   +2 more
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Bayesian Estimation of Gaussian Conditional Random Fields

Statistica Sinica, 2022
Summary: We propose a novel methodology based on a Bayesian Gaussian conditional random field model for elegantly learning the conditional dependence structures among multiple outcomes, and between the outcomes and a set of covariates simultaneously. Our approach is based on a Bayesian hierarchical model using a spike and slab Lasso prior.
Gan, Lingrui, Narisetty, Naveen, Liang, Feng   +2 more
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Staging tissues with conditional random fields

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011
We present a framework for identifying disease states by classifying cells in the pathological regions of tissues into different categories. We use conditional random fields (CRF) to incorporate characteristics of cells and their spatial distributions.
Jagath C, Rajapakse, Song, Liu
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Conditional Topic Random Fields

2010
Generative topic models such as LDA are limited by their inability to utilize nontrivial input features to enhance their performance, and many topic models assume that topic assignments of different words are conditionally independent. Some work exists to address the second limitation but no work exists to address both.
Zhu, Jun, Xing, Eric P.
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Triangular-Chain Conditional Random Fields

IEEE Transactions on Audio, Speech, and Language Processing, 2008
Sequential modeling is a fundamental task in scientific fields, especially in speech and natural language processing, where many problems of sequential data can be cast as a sequential labeling or a sequence classification. In many applications, the two problems are often correlated, for example named entity recognition and dialog act classification ...
Jeong, M, Lee, GG
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Conditioned Stochastic Processes for Conditional Random Fields

Journal of Engineering Mechanics, 1994
Analytical development is presented for the theory of conditional random fields involving conditioning deterministic time functions. After discussion of their basic concept and their engineering significance, the probability distribution of the Fourier coefficients for conditioned stochastic processes is derived.
Kameda, H., Morikawa, H.
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Infinite Latent Conditional Random Fields

2013 IEEE International Conference on Computer Vision Workshops, 2013
In this paper, we present Infinite Latent Conditional Random Fields (ILCRFs) that model the data through a mixture of CRFs generated from Dirichlet processes. Each CRF represents one possible explanation of the data. In addition to visible nodes and edges that exist in classic CRFs, it generatively models the distribution of different CRF structures ...
Yun Jiang, Ashutosh Saxena
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Conditional cyclic Markov random fields

Advances in Applied Probability, 1996
Grenanderet al.(1991) proposed a conditional cyclic Gaussian Markov random field model for the edges of a closed outline in the plane. In this paper the model is recast as an improper cyclic Gaussian Markov random field for the vertices. The limiting behaviour of this model when the vertices become closely spaced is also described and in particular its
Kent, John T., Mardia, Kanti V., Walder, Alistair N.   +2 more
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Left Atrial Appendage Segmentation Using Fully Convolutional Neural Networks and Modified Three-Dimensional Conditional Random Fields

IEEE journal of biomedical and health informatics, 2018
Thrombosis has become a global disease threatening human health. The left atrial appendage (LAA) is a major source of thrombosis in patients with atrial fibrillation (AF). Positive correlation exists between LAA volume and AF risk.
Cheng Jin, Jianjiang Feng, Lei Wang, Heng Yu, Jiang Liu, Jiwen Lu, Jie Zhou   +6 more
semanticscholar   +1 more source

Learning Flexible Features for Conditional Random Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Extending traditional models for discriminative labeling of structured data to include higher-order structure in the labels results in an undesirable exponential increase in model complexity. In this paper, we present a model that is capable of learning such structures using a random field of parameterized features.
Liam, Stewart, Xuming, He, Richard S, Zemel   +2 more
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