Geoffrey G. Towell

Model Architectures · 1989–2004

A researcher in the Science corpus: 5 papers; trusts 6 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Model Architectures — 16 citations into it

Whom Geoffrey G. Towell trusts overall

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

1. Geoffrey E. Hinton
2. David E. Rumelhart
3. Steven J. Nowlan
4. Sally A. Goldman
5. Ronald J. Williams
6. Michael C. Mozer
7. Yann LeCun
8. Michael Kearns
9. Fernando J. Pineda
10. Michael J. Pazzani

Who trusts Geoffrey G. Towell overall

1. J. Urgen Schmidhuber
2. A.S. d'Avila Garcez
3. Wlodzislaw Duch
4. Jude W. Shavlik
5. Rudy Setiono
6. Pedro M. Domingos
7. Marko Robnik-Sikonja
8. Tameru Hailesilassie
9. Henrik Jacobsson
10. Lior Rokach

Papers from Geoffrey G. Towell's perspective

Ordered by how strongly the researchers Geoffrey G. Towell 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Extracting refined rules from knowledge-based neural networks (2004) — 297 citations
• Knowledge-Based Artificial Neural Networks (1994)
• Extracting Refined Rules from Knowledge-Based Neural Networks (1993)
• An Approach to Combining Explanation-based and Neural Learning Algorithms (1989)
• Combining Explanation-Based and Neural Learning: An Algorithm and Empirical Results (1989)