Michael Finke

Scaling & Learning Theory · 1995–1997

A researcher in the Science corpus: 5 papers; trusts 8 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Scaling & Learning Theory — 7 citations into it

Whom Michael Finke trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Shun-ichi Amari
2. Michael I. Jordan
3. Robert A. Jacobs
4. Arthur P. Dempster
5. Eric B. Baum
6. Hirotugu Akaike
7. Jorma Rissanen
8. Geoffrey E. Hinton

Who trusts Michael Finke overall

1. Juan Shen
2. Riki Eto
3. Lutz Prechelt
4. Lucas Caccia

Papers from Michael Finke's perspective

Ordered by how strongly the researchers Michael Finke trusts endorse each paper. Same corpus, a different order for every researcher.

1. Learning internal representations by error propagation
2. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper
3. Hierarchical Mixtures of Experts and the EM Algorithm
4. Adaptive Mixtures of Local Experts
5. ImageNet classification with deep convolutional neural networks
6. A new look at the statistical model identification
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Gradient-based learning applied to document recognition
9. Regularization and complexity control in feed-forward networks
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Asymptotic statistical theory of overtraining and cross-validation (1997)
• Janus-III: speech-to-speech translation in multiple languages (1997)
• Adaptively Growing Hierarchical Mixtures of Experts (1996)
• A Numerical Study on Learning Curves in Stochastic Multilayer Feedforward Networks (1996)
• Statistical Theory of Overtraining - Is Cross-Validation Asymptotically Effective? (1995)