David W. Romero

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 509 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 125 citations into it; #11,426 of 21,575 by trust here, #16,787 by citations
• Artificial Intelligence — 102 citations into it; #18,166 of 35,345 by trust here, #27,551 by citations
• Model Architectures — 91 citations into it
• Computational Mechanics — 18 citations into it
• Natural Language Processing — 18 citations into it
• Computational Theory and Mathematics — 14 citations into it
• Signal Processing — 14 citations into it
• 3D Vision & Pose — 11 citations into it

Whom David W. Romero trusts overall

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

1. Taco Cohen
2. Alex Krizhevsky
3. Max Welling
4. Diederik P. Kingma
5. Maurice Weiler
6. Geoffrey E. Hinton
7. Yoshua Bengio
8. Kaiming He
9. Daniel E. Worrall
10. Yann LeCun

Who trusts David W. Romero overall

1. Erik J. Bekkers
2. J. Tomczak
3. Fabian B. Fuchs
4. Mark Hoogendoorn
5. Jean-Baptiste Cordonnier
6. Derya Soydaner
7. Daniel E. Worrall

Papers from David W. Romero's perspective

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

Their papers

• Attentive Group Equivariant Convolutional Networks (2020) — 102 citations
• Group Equivariant Stand-Alone Self-Attention For Vision (2020) — 77 citations
• Wavelet Networks: Scale Equivariant Learning From Raw Waveforms (2020) — 18 citations
• Co-Attentive Equivariant Neural Networks: Focusing Equivariance On Transformations Co-Occurring In Data (2019) — 25 citations