The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Mean Field Theory for Sigmoid Belief Networks
Open accessAbstract
We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean field theory provides a tractable approximation to the true probability distribution in these networks; it also yields a lower bound on the likelihood of evidence. We demonstrate the utility of this framework on a benchmark problem in statistical pattern recognition---the classification of handwritten digits.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
A learning algorithm for Boltzmann machines
Ackley, D. H., Hinton, G. E., & Sejnowski, T. J. (1985) · Cognitive Science · doi:10.1207/s15516709cog0901_7 · Cited by 3,402
Itzykson, C., & Drouffe, J.-M. (1989) · doi:10.1017/cbo9780511622779 · Cited by 885
Cited by
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 4,941
An Introduction to Variational Methods for Graphical Models
Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K. (1999) · Machine Learning · doi:10.1023/a:1007665907178 · Cited by 3,791
Self-citation(2003) · Cited by 1,680
Factorial Hidden Markov Models
Ghahramani, Z., & Jordan, M. I. (1997) · Machine Learning · doi:10.1023/a:1007425814087 · Cited by 1,157
Self-citationMOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data
Argelaguet, R., Arnol, D., Bredikhin, D., Deloro, Y., Velten, B., Marioni, J. C., & Stegle, O. (2020) · Genome Biology · doi:10.1186/s13059-020-02015-1 · Cited by 1,091
Dynamic Model of Visual Recognition Predicts Neural Response Properties in the Visual Cortex
Rao, R. P. N., & Ballard, D. H. (1997) · Neural Computation · doi:10.1162/neco.1997.9.4.721 · Cited by 290
BILMES, J. A. (2006) · IEICE Transactions on Information and Systems · doi:10.1093/ietisy/e89-d.3.869 · Cited by 172
Deep Learning: A Bayesian Perspective
Polson, N. G., & Sokolov, V. (2017) · Bayesian Analysis · doi:10.1214/17-ba1082 · Cited by 120
Quantifying uncertainty in deep learning approaches to radio galaxy classification
Mohan, D., Scaife, A. M. M., Porter, F., Walmsley, M., & Bowles, M. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac223 · Cited by 16
Metadata
- Authors
- Saul, L. K., Jaakkola, T., & Jordan, M. I.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 1996-03-01
- Volume
- 4
- Pages
- 61-76
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W1520448186
- Cited by
- 327
- References
- 3
- FWCI
- 20.67
- Type
- journal article
- Language
- en
- Open access
- diamond
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
