Adaptive Mixtures of Local Experts
Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan low, Geoffrey E. Hinton
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Citation edge evidence
- Distilling the Knowledge in a Neural Network→Adaptive Mixtures of Local ExpertsLegacy edge — source detail unavailable
- Finite Mixture Models→Adaptive Mixtures of Local ExpertsLegacy edge — source detail unavailable
- Improving neural networks by preventing co-adaptation of feature detectors→Adaptive Mixtures of Local ExpertsLegacy edge — source detail unavailable
- Ensemble learning: A survey→Adaptive Mixtures of Local ExpertsLegacy edge — source detail unavailable
- Popular Ensemble Methods: An Empirical Study→Adaptive Mixtures of Local ExpertsLegacy edge — source detail unavailable
- Ensemble based systems in decision making→Adaptive Mixtures of Local ExpertsLegacy edge — source detail unavailable
- Hierarchical Mixtures of Experts and the EM Algorithm→Adaptive Mixtures of Local ExpertsLegacy edge — source detail unavailable
- Universal Approximation using Incremental Constructive Feedforward Networks with Random H…→Adaptive Mixtures of Local ExpertsLegacy edge — source detail unavailable
- Ensembling neural networks: Many could be better than all→Adaptive Mixtures of Local ExpertsLegacy edge — source detail unavailable
- Building Watson: An Overview of the DeepQA Project→Adaptive Mixtures of Local ExpertsLegacy edge — source detail unavailable
- Adaptive Computation and Machine Learning→Adaptive Mixtures of Local ExpertsLegacy edge — source detail unavailable
- Adaptive Mixtures of Local Experts→Fast Learning in Networks of Locally-Tuned Processing UnitsLegacy edge — source detail unavailable
- Adaptive Mixtures of Local Experts→Mixture models : inference and applications to clusteringLegacy edge — source detail unavailable
- Distilling the Knowledge in a Neural Network→Improving neural networks by preventing co-adaptation of feature detectorsLegacy edge — source detail unavailable
- Ensembling neural networks: Many could be better than all→Hierarchical Mixtures of Experts and the EM AlgorithmLegacy edge — source detail unavailable
- Ensembling neural networks: Many could be better than all→Popular Ensemble Methods: An Empirical StudyLegacy edge — source detail unavailable
- Universal Approximation using Incremental Constructive Feedforward Networks with Random H…→Hierarchical Mixtures of Experts and the EM AlgorithmLegacy edge — source detail unavailable
- Ensemble based systems in decision making→Hierarchical Mixtures of Experts and the EM AlgorithmLegacy edge — source detail unavailable
- Ensemble learning: A survey→Ensembling neural networks: Many could be better than allLegacy edge — source detail unavailable
- Ensemble learning: A survey→Popular Ensemble Methods: An Empirical StudyLegacy edge — source detail unavailable
- Ensemble learning: A survey→Ensemble based systems in decision makingLegacy edge — source detail unavailable
What this paper cites, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Fast Learning in Networks of Locally-Tuned Processing Units | 1989 | 4,230 |
| Mixture models : inference and applications to clustering | 1988 | 2,079 |
What cites it, inside the corpus
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Topics
| Neural Networks and Applications | Computer Science |
| Music and Audio Processing | Computer Science |
| Speech and Audio Processing | Computer Science |
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