Neil D. B. Bruce

Computer Vision and Pattern Recognition · 2005–2021

A researcher in the Science corpus: 7 papers; trusts 596 researchers and is trusted by 61 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 111 citations into it; #3,817 of 21,575 by trust here, #5,161 by citations
• Artificial Intelligence — 53 citations into it
• Model Architectures — 25 citations into it; #1,880 of 2,278 by trust here, #1,317 by citations
• Cognitive Neuroscience — 21 citations into it; #1,598 of 10,852 by trust here, #5,162 by citations
• Safety, Robustness & Interpretability — 13 citations into it; #1,854 of 2,251 by trust here, #1,265 by citations
• Aerospace Engineering — 8 citations into it
• Representation & Self-Supervised Learning — 8 citations into it
• Biophysics — 6 citations into it

Whom Neil D. B. Bruce trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Karen Simonyan
4. Zhaoping Li
5. Alex Krizhevsky
6. Yoshua Bengio
7. Ross B. Girshick
8. Andrew Zisserman
9. Laurent Itti
10. Yann LeCun

Who trusts Neil D. B. Bruce overall

1. Wenguan Wang
2. Ali Borji
3. Xiaodi Hou
4. Jianbing Shen
5. Ming-Ming Cheng
6. Jonathan Harel
7. Erkut Erdem
8. Tilke Judd
9. Jianming Zhang
10. Congyan Lang

Papers from Neil D. B. Bruce's perspective

Ordered by how strongly the researchers Neil D. B. Bruce trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Gated Feedback Refinement Network for Dense Image Labeling (2017) — 210 citations
• How Much Position Information Do Convolutional Neural Networks Encode? (2020) — 408 citations
• Saliency, attention, and visual search: an information theoretic approach. (2009) — 944 citations
• Shape or Texture: Understanding Discriminative Features in CNNs (2021) — 93 citations
• Global Pooling, More than Meets the Eye: Position Information is Encoded Channel-Wise in CNNs (2021) — 36 citations
• Malware Classification with Deep Convolutional Neural Networks (2018) — 354 citations
• Saliency Based on Information Maximization (2005) — 1,278 citations