Results 281 to 290 of about 12,817,495 (340)

Generalized LDPC codes and generalized stopping sets

IEEE Transactions on Communications, 2008
A generalized low-density parity check code (GLDPC) is a low-density parity check code in which the constraint nodes of the code graph are block codes, rather than single parity checks. In this paper, we study GLDPC codes which have BCH or Reed-Solomon codes as subcodes under bounded distance decoding (BDD).
Nenad Miladinovic, Marc P. C. Fossorier
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Stopping and Trapping Sets in Generalized Covering Arrays

2006 40th Annual Conference on Information Sciences and Systems, 2006
Certain combinatorial structures embedded in the parity-check matrix of linear codes, such as stopping and trapping sets, are known to govern the behavior of the codes' bit error rate curves under iterative decoding. We show how the Lovasz local lemma can be used to obtain epsiv-probability bounds on the frequency of occurrence of such structures.
Olgica Milenkovic, Emina Soljanin, Philip Whiting   +2 more
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Small stopping sets in Steiner triple systems

Cryptography and Communications, 2008
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Charles J. Colbourn, Yuichiro Fujiwara
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On the complexity of and solutions to the minimum stopping and trapping set problems

Theoretical Computer Science, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Alvaro Velasquez, K. Subramani 0001, Piotr Wojciechowski 0002   +2 more
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Stopping sets for MDS-based product codes

2016 IEEE International Symposium on Information Theory (ISIT), 2016
Stopping sets for MDS-based product codes under iterative row-column algebraic decoding are analyzed in this paper. A union bound to the performance of iterative decoding is established for the independent symbol erasure channel. This bound is tight at low and very low error rates.
Fanny Jardel, Joseph Jean Boutros, Mireille Sarkiss   +2 more
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Stopping Bayesian Optimization with Probabilistic Regret Bounds

Neural Information Processing Systems
Bayesian optimization is a popular framework for efficiently tackling black-box search problems. As a rule, these algorithms operate by iteratively choosing what to evaluate next until some predefined budget has been exhausted.
James T. Wilson
semanticscholar   +1 more source

A Notion of Stopping Line for Set-Indexed Processes

Journal of Theoretical Probability, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Saada, Diane, Slonowsky, Dean
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Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks

International Conference on Artificial Intelligence and Statistics, 2019
Modern neural networks are typically trained in an over-parameterized regime where the parameters of the model far exceed the size of the training data.
Ming-Chen Li, M. Soltanolkotabi, Samet Oymak   +2 more
semanticscholar   +1 more source

Stopping sets in codes from designs

IEEE International Symposium on Information Theory, 2003. Proceedings., 2003
The size of the smallest stopping set in LDPC codes helps in analyzing their performance under iterative decoding, just a minimum distance helps in analyzing the performance under maximum likelihood decoding. We study stopping sets in LDPC codes arising from 2-designs, in particular LDPC codes derived from projective and Euclidean geometries. We derive
N. Kashyap, A. Vardy
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Stopping sets and the girth of Tanner graphs

Proceedings IEEE International Symposium on Information Theory,, 2003
Recent work has related the error probability of iterative decoding over erasure channels to the presence of stopping sets in the Tanner graph of the code used. In particular, it was shown that the smallest number of uncorrected erasures is the size of the graph's smallest stopping set. Relating stopping sets and girths, we consider the size /spl sigma/
A. Orlitsky, R. Urbanke, K. Viswanathan, J. Zhang   +3 more
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