The DOI matches its Crossref record; its reference list is not available.
Free energy minimisation algorithmfor decoding and cryptanalysis
Abstract
An algorithm is derived for inferring a binary vector s given noisy observations of As modulo 2, where A is a binary matrix. The binary vector is replaced by a vector of probabilities, optimised by free energy minimisation. Experiments on the inference of the state of a linear feedback shift register indicate that this algorithm supersedes the Meier and Staffelbach polynomial algorithm.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Variational Bayes approximates the conditional density by optimising sufficient statistics of an approximate density with respect to a lower bound on evidence.
“Variational Bayes is a generic approach to model inversion that approximates the conditional density p(ϑ|y,m) on some model parameters, ϑ, given a model m and data y.”
From Hierarchical Models in the Brain (2008) · cites this paper as [13]
Free-energy is an information theory quantity that bounds the evidence for a model of data.
“In statistics and machine learning, free-energy is an information theory quantity that bounds the evidence for a model of data (Hinton and von Camp 1993;MacKay 1995;Neal and Hinton 1998).”
From Action and behavior: a free-energy formulation (2010) · cites this paper as MacKay 1995
Citations
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 6 · Cited by 79
Research works printed but not linked: 5
- Meier, 1989, 1, 159, 10.1007/BF02252874 · DOI
- Anderson, R.J.: ‘Searching for the optimum correlation attack’, Preneel, B., Proc. of 1994 K.U.Leuven Workshop on Cryptographic Algorithms, Lecture Notes in Computer Science, (Springer-Verlag 1995) · DOI
- Hopfield, 1985, 52, 1, 10.1007/BF00336930 · DOI
- Van den Bout, 1989, 62, 129, 10.1007/BF00203001 · DOI
- MacKay, 1995, Proc. 1994 K.U.Leuven Workshop on Cryptographic Algorithms
Metadata
- Authors
- MacKay, D. J. C.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- MacKay, D.J.C.
- ORCID
- MacKay, D.J.C.
- Venue
- Electronics Letters
- Published
- 1995-03-16
- Volume
- 31
- Issue
- 6
- Pages
- 446-447
- ISSN
- 0013-5194 (ISSN-L)
- 1350-911X
- OpenAlex
- W2135100787
- Cited by
- 79
- References
- 6
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2023: 3 · 2022: 7 · 2021: 8 · 2020: 10 · 2019: 2 · 2018: 1 · 2017: 3 · 2014: 1 · 2013: 3 · 2012: 4
- FWCI
- 0.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Electronics Letters (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Discrete Mathematics and Combinatorics › Coding theory and cryptography
- Physical Sciences › Computer Science › Computer Networks and Communications › Error Correcting Code Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Cryptographic Implementations and Security
- Keywords
- free energy minimization, noisy measurements, binary matrices, probabilistic decoding, cryptanalysis, linear feedback shift registers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
