2015
Olmos, Pablo M; Urbanke, Rudiger
A Scaling Law to Predict the Finite-Length Performance of Spatially-Coupled LDPC Codes Artículo de revista
En: IEEE Transactions on Information Theory, vol. 61, no 6, pp. 3164–3184, 2015, ISSN: 0018-9448.
Resumen | Enlaces | BibTeX | Etiquetas: asymptotic analysis, asymptotic properties, binary erasure channel, Channel Coding, Codes on graphs, Couplings, Decoding, Differential equations, error probability, finite length performance, finite length spatially coupled code, finite-length code performance, finite-length performance, Iterative decoding, iterative decoding thresholds, Journal, parity check codes, Probability, SC-LDPC codes, scaling law, Sockets, spatially coupled LDPC codes, spatially-coupled LDPC codes
@article{Olmos2015bb,
title = {A Scaling Law to Predict the Finite-Length Performance of Spatially-Coupled LDPC Codes},
author = {Pablo M Olmos and Rudiger Urbanke},
url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=7086074},
doi = {10.1109/TIT.2015.2422816},
issn = {0018-9448},
year = {2015},
date = {2015-06-01},
journal = {IEEE Transactions on Information Theory},
volume = {61},
number = {6},
pages = {3164--3184},
abstract = {Spatially-coupled low-density parity-check (SC-LDPC) codes are known to have excellent asymptotic properties. Much less is known regarding their finite-length performance. We propose a scaling law to predict the error probability of finite-length spatially coupled code ensembles when transmission takes place over the binary erasure channel. We discuss how the parameters of the scaling law are connected to fundamental quantities appearing in the asymptotic analysis of these ensembles and we verify that the predictions of the scaling law fit well to the data derived from simulations over a wide range of parameters. The ultimate goal of this line of research is to develop analytic tools for the design of SC-LDPC codes under practical constraints.},
keywords = {asymptotic analysis, asymptotic properties, binary erasure channel, Channel Coding, Codes on graphs, Couplings, Decoding, Differential equations, error probability, finite length performance, finite length spatially coupled code, finite-length code performance, finite-length performance, Iterative decoding, iterative decoding thresholds, Journal, parity check codes, Probability, SC-LDPC codes, scaling law, Sockets, spatially coupled LDPC codes, spatially-coupled LDPC codes},
pubstate = {published},
tppubtype = {article}
}
2013
Alvarez, Mauricio; Luengo, David; Lawrence, Neil D
Linear Latent Force Models Using Gaussian Processes Artículo de revista
En: IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no 11, pp. 2693–2705, 2013.
Resumen | Enlaces | BibTeX | Etiquetas: Analytical models, Computational modeling, Data models, Differential equations, Force, Gaussian processes, Mathematical mode
@article{Alvarez2013,
title = {Linear Latent Force Models Using Gaussian Processes},
author = {Mauricio Alvarez and David Luengo and Neil D Lawrence},
url = {http://dblp.uni-trier.de/db/journals/pami/pami35.html#AlvarezLL13 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6514873},
year = {2013},
date = {2013-01-01},
journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
volume = {35},
number = {11},
pages = {2693--2705},
abstract = {Purely data-driven approaches for machine learning present difficulties when data are scarce relative to the complexity of the model or when the model is forced to extrapolate. On the other hand, purely mechanistic approaches need to identify and specify all the interactions in the problem at hand (which may not be feasible) and still leave the issue of how to parameterize the system. In this paper, we present a hybrid approach using Gaussian processes and differential equations to combine data-driven modeling with a physical model of the system. We show how different, physically inspired, kernel functions can be developed through sensible, simple, mechanistic assumptions about the underlying system. The versatility of our approach is illustrated with three case studies from motion capture, computational biology, and geostatistics.},
keywords = {Analytical models, Computational modeling, Data models, Differential equations, Force, Gaussian processes, Mathematical mode},
pubstate = {published},
tppubtype = {article}
}