Christopher J. Rozell

Artificial Intelligence · 2008–2020

A researcher in the Science corpus: 2 papers; trusts 27 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 45 citations into it
• Safety, Robustness & Interpretability — 28 citations into it
• Computer Vision and Pattern Recognition — 27 citations into it
• Computational Mechanics — 9 citations into it; #7,410 of 10,001 by trust here, #4,178 by citations
• Generative Models — 6 citations into it
• Natural Language Processing — 6 citations into it
• Signal Processing — 5 citations into it

Whom Christopher J. Rozell trusts overall

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

1. David L. Donoho
2. E.J. Candes
3. Joel A. Tropp
4. David J. Field
5. Bhaskar D. Rao
6. John J. Hopfield
7. Ole W. Christensen
8. Kenneth Kreutz-Delgado
9. Mário A. T. Figueiredo
10. George Eastman House

Who trusts Christopher J. Rozell overall

1. Zhaowen Wang
2. Karol Gregor
3. Klaus Greff
4. Yann LeCun
5. Madhu S. Advani
6. Djork-Arné Clevert
7. Ding Liu
8. Surya Ganguli
9. Patrick Sheridan
10. Matthew D. Zeiler

Papers from Christopher J. Rozell's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
8. Long Short-Term Memory
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Generative causal explanations of black-box classifiers (2020) — 83 citations
• Sparse Coding via Thresholding and Local Competition in Neural Circuits (2008) — 515 citations