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An Introduction to Variational Methods for Graphical Models
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A variety of analytic approximations exist which ameliorate, but do not remove, the tradeoff between tractability and flexibility.
“A variety of analytic approximations exist which ameliorate, but do not remove, this tradeoff”
From Deep Unsupervised Learning using Nonequilibrium Thermodynamics (2015) · cites this paper as Jordan et al., 1999
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Maximum Likelihood from Incomplete Data Via the EM Algorithm
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Keeping the neural networks simple by minimizing the description length of the weights
Hinton, G. E., & van Camp, D. (1993) · Proceedings of the sixth annual conference on Computational learning theory - COLT '93 · doi:10.1145/168304.168306 · Cited by 994
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Metadata
- Authors
- Jordan, M. I., Ghahramani, Z., Jaakkola, T. S., & Saul, L. K.
- Affiliations
- University of California, Berkeley (US)
- Gatsby Computational Neuroscience Unit (GB)
- Massachusetts Institute of Technology (US)
- AT&T (United States) (US)
- Venue
- Machine Learning
- Published
- 1999-11
- Volume
- 37
- Issue
- 2
- Pages
- 183-233
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1516111018
- Cited by
- 3,791
- References
- 2
- Citations by year
- 2026: 148 · 2025: 158 · 2024: 132 · 2023: 137 · 2022: 142 · 2021: 244 · 2020: 315 · 2019: 298 · 2018: 217 · 2017: 182 · 2016: 162 · 2015: 182 · 2014: 151 · 2013: 141 · 2012: 173
- FWCI
- 35.87
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Keywords
- variational methods, probabilistic graphical models, Bayesian networks, Markov random fields, approximate inference, convex duality, Boltzmann machine, hidden Markov models, probabilistic learning
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