Automorphisms of Order 2p in Binary Self-Dual Extremal Codes of Length a Multiple of 24

By Martino Borello, W. Willems. 2013

1,317 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Sociology and Political Science, Information Theory, Algorithms & Complexity

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Citations in the corpus

JIDT: An Information-Theoretic Toolkit for Studying the Dynamics of Complex Systems (2014, background): “We discuss several such estimators in the following.”
Overhead and noise threshold of fault-tolerant quantum error correction (2002, neutral): “Table 2 gives the ﬁtted coeﬃcient a in the single-parameter linear ﬁt P w =1 = aγ , and a ′ in the ﬁt P w =2 + P w”
To understand deep learning we need to understand kernel learning (2018, background): “A notable (and, to the best of our knowledge, the only) example is 1-nearest neighbor classifier, with expected loss that can be bounded asymptotically by twice the Bayes risk [CH67], while its empirical loss (both classification and regression) is identically zero.”
Probabilistic reconstruction in compressed sensing: algorithms, phase diagrams, and threshold achieving matrices (2012, methodology): “(2012) P 08009 Probabilistic reconstruction in compressed sensing Appendix A. Derivation of the replica analysis for block matrices 50 Appendix B. Phase diagram of the `1 reconstruction for seeding matrices 52 Appendix C. Equations for a mixture of Gaussians 54 References 55”
End-to-end Optimized Image Compression (2016, methodology): “…are only slightly larger than the entropy (Rissanen and Langdon, 1981), and thus we deﬁne the objective functional directly in terms of entropy: L [ g a , g s , P q ] = − E log 2 P q + λ E d ( z , ˆ z ) , (7) where both expectations will be approximated by averages over a training set of images.”
Minimax Rates of Entropy Estimation on Large Alphabets via Best Polynomial Approximation (2014, methodology): “Paninski also investigated using approximation by Bernstein polynomials to reduce the bias of the plug-in estimators [Pan03], which forms the basis for proving the existence of consistent estimators with sublinear sample complexity in [Pan04].”
A framework for Bayesian optimality of psychophysical laws (2012, methodology): “The discretization of a continuous scalar is called quantization and is well-studied under a Bayesian framework (Gray & Neuhoff, 1998).”
Unsupervised Continual Learning Via Pseudo Labels (2021, methodology): “In this work, we adopt K-means [Lloyd, 1982] as our global clustering algorithm for illustration purpose and we propose to use the continual learning model (except the last fully connected layers) at every incremental step for feature extraction of unlabeled data to obtain pseudo label.”
Disciplined convex-concave programming (2016, background): “, n, where x is the optimization variable [For72].”