John E. Moody

Reinforcement Learning · 1988–2008

A researcher in the Science corpus: 8 papers; trusts 17 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 10 citations into it
• Statistics and Probability — 6 citations into it
• Model Architectures — 5 citations into it

Whom John E. Moody trusts overall

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

1. Hirotugu Akaike
2. Ronald J. Williams
3. Brian S. Yandell
4. Paul J. Werbos
5. Richard S. Sutton
6. Halbert White
7. Jorma Rissanen
8. S. Geisser
9. Michael C. Mozer
10. David Zipser

Who trusts John E. Moody overall

1. J. Urgen Schmidhuber
2. Volker Tresp
3. V. Roshan Joseph
4. M. V. D. Baan
5. David W. Opitz
6. Yoshua Bengio
7. Zhengyao Jiang
8. Sergey Levine
9. Samuel Müller
10. Yuxi Li

Papers from John E. Moody's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Learning internal representations by error propagation
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Reinforcement Learning: An Introduction
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning to trade via direct reinforcement (2001) — 560 citations
• Predicting Structured Data (2008)
• Reinforcement Learning for Trading (1998)
• Architecture Selection Strategies for Neural Networks: Application to Corporate Bond Rating Predicti (1995)
• Fast Pruning Using Principal Components (1993)
• The Effective Number of Parameters: An Analysis of Generalization and Regularization in Nonlinear Learning Systems (1991)
• Note on Learning Rate Schedules for Stochastic Optimization (1990)
• Fast Learning in Multi-Resolution Hierarchies (1988)