Keeping the neural networks simple by minimizing the description length of the weights
COLT '93 Proceedings of the sixth annual conference on Computational learning theory
Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
Natural gradient works efficiently in learning
Neural Computation
On-line learning and stochastic approximations
On-line learning in neural networks
Learning in graphical models
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
An Introduction to Variational Methods for Graphical Models
Machine Learning
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Introduction to Stochastic Search and Optimization
Introduction to Stochastic Search and Optimization
Variational methods for inference and estimation in graphical models
Variational methods for inference and estimation in graphical models
The Journal of Machine Learning Research
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Monte Carlo Statistical Methods (Springer Texts in Statistics)
Online Model Selection Based on the Variational Bayes
Neural Computation
ICML '06 Proceedings of the 23rd international conference on Machine learning
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
An HDP-HMM for systems with state persistence
Proceedings of the 25th international conference on Machine learning
Bayesian probabilistic matrix factorization using Markov chain Monte Carlo
Proceedings of the 25th international conference on Machine learning
Natural Conjugate Gradient in Variational Inference
Neural Information Processing
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Nonparametric factor analysis with beta process priors
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Mean field theory for sigmoid belief networks
Journal of Artificial Intelligence Research
Learning semantic correspondences with less supervision
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Distributed Algorithms for Topic Models
The Journal of Machine Learning Research
Online Learning for Matrix Factorization and Sparse Coding
The Journal of Machine Learning Research
On smoothing and inference for topic models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
An architecture for parallel topic models
Proceedings of the VLDB Endowment
Larger residuals, less work: active document scheduling for latent dirichlet allocation
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Inferring parameters and structure of latent variable models by variational bayes
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Expectation-propagation for the generative aspect model
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Variational approximations between mean field theory and the junction tree algorithm
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
A generalized mean field algorithm for variational inference in exponential families
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Scalable inference in latent variable models
Proceedings of the fifth ACM international conference on Web search and data mining
Communications of the ACM
Bayesian Nonparametric Inference of Switching Dynamic Linear Models
IEEE Transactions on Signal Processing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational Bayesian Approach to Canonical Correlation Analysis
IEEE Transactions on Neural Networks
Machine Learning: A Probabilistic Perspective
Machine Learning: A Probabilistic Perspective
Dynamic multi-faceted topic discovery in twitter
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets.