David Zipser

Optimization & Training · 1986–1998

A researcher in the Science corpus: 7 papers; trusts 13 researchers and is trusted by 335 researchers here, as inferred from the citation graph.

Subfields

• Optimization & Training — 9 citations into it; #329 of 2,601 by trust here, #822 by citations
• Model Architectures — 7 citations into it; #101 of 2,278 by trust here, #248 by citations

Whom David Zipser trusts overall

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

1. Ronald J. Williams
2. A. Robinson
3. Paul J. Werbos
4. Barak A. Pearlmutter
5. David E. Rumelhart
6. Michael C. Mozer
7. F. Fallside
8. Richard James Rohwer
9. Geoffrey E. Hinton
10. J. Urgen Schmidhuber

Who trusts David Zipser overall

1. J. Urgen Schmidhuber
2. Yoshua Bengio
3. Ronald J. Williams
4. Sergey Levine
5. Alex Graves
6. Xinlei Chen
7. Trevor Darrell
8. Mohit Bansal
9. Quoc V. Le
10. Ruslan Salakhutdinov

Papers from David Zipser's perspective

Ordered by how strongly the researchers David Zipser 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Learning internal representations by error propagation

Their papers

• Analysis of Direction Selectivity Arising from Recurrent Cortical Interactions (1998)
• Gradient-based learning algorithms for recurrent networks and their computational complexity (1995)
• A spiking network model of short-term active memory (1993)
• Gradient-based learning algorithms for recurrent connectionist networks (1990)
• Experimental Analysis of the Real-time Recurrent Learning Algorithm (1989)
• A Learning Algorithm for Continually Running Fully Recurrent Neural Networks (1989)
• Feature Discovery by Competive Learning (1986)