Results 51 to 60 of about 168,985 (282)
Markov random fields and Markov chains on trees [PDF]
, 1981 We consider probability measures on a space S(^A) (where S and A are countable and the σ-field is the natural one) which are Markov random fields with respect to a given neighbour relation ~ on A.
Zachary, Stan
core
Sensing Capacity for Markov Random Fields
, 2005 This paper computes the sensing capacity of a sensor network, with sensors of limited range, sensing a two-dimensional Markov random field, by modeling the sensing operation as an encoder.
Khosla, Pradeep +2 more
core +3 more sources
Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image [PDF]
, 2013 Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction
Benediktsson, Jon Atli +3 more
core +3 more sources
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla +4 more
wiley +1 more source
Unsupervised Bilingual POS Tagging with Markov Random Fields [PDF]
, 2011 In this paper, we give a treatment to the problem of bilingual part-of-speech induction with parallel data. We demonstrate that naïve optimization of log-likelihood with joint MRFs suffers from a severe problem of local maxima, and suggest an alternative
Chen, Desai +3 more
core +4 more sources
Texture Modelling with Nested High-order Markov-Gibbs Random Fields
, 2015 Currently, Markov-Gibbs random field (MGRF) image models which include high-order interactions are almost always built by modelling responses of a stack of local linear filters.
Gimel'farb, Georgy +2 more
core +1 more source
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
Advanced Intelligent Discovery, EarlyView.The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
Linear and Parallel Learning of Markov Random Fields [PDF]
, 2014 We introduce a new embarrassingly parallel parameter learning algorithm for Markov random fields with untied parameters which is efficient for a large class of practical models. Our algorithm parallelizes naturally over cliques and, for graphs of bounded
de Freitas, Nando +2 more
core +1 more source
Telescoping Recursive Representations and Estimation of Gauss-Markov Random Fields
, 2010 We present \emph{telescoping} recursive representations for both continuous and discrete indexed noncausal Gauss-Markov random fields. Our recursions start at the boundary (a hypersurface in $\R^d$, $d \ge 1$) and telescope inwards.
Moura, Jose M. F., Vats, Divyanshu
core +2 more sources