David J. Wu

Computer Vision and Pattern Recognition · with Coates · 2011–2013

A researcher in the Science corpus: 4 papers; trusts 50 researchers and is trusted by 319 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 39 citations into it; #2,280 of 21,575 by trust here, #5,464 by citations
• Artificial Intelligence — 24 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Optimization & Training — 7 citations into it; #1,690 of 2,601 by trust here, #626 by citations
• Privacy & Security — 7 citations into it
• Computational Theory and Mathematics — 5 citations into it
• Generative Models — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom David J. Wu trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Alex Krizhevsky
4. Adam Coates
5. Craig Gentry
6. Aapo Hyvärinen
7. Yoshua Bengio
8. Andrew Y. Ng
9. Zvika Brakerski
10. Quoc V. Le

Who trusts David J. Wu overall

1. Pedro M. Esperança
2. Louis J. M. Aslett
3. Max Jaderberg
4. Praneeth Vepakomma
5. Xiang Bai
6. Pengtao Xie
7. Andrea Vedaldi
8. Baoguang Shi
9. Chris C. Holmes
10. Cong Yao

Papers from David J. Wu's perspective

Ordered by how strongly the researchers David J. Wu 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. 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• End-to-end text recognition with convolutional neural networks (2012) — 981 citations
• Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning (2011) — 437 citations
• Deep learning with COTS HPC systems (2013) — 755 citations
• Using Homomorphic Encryption for Large Scale Statistical Analysis (2012) — 56 citations