Daniel D. Lee

Computer Vision and Pattern Recognition · International Conference on Machine Learning · 2003–2015

A researcher in the Science corpus: 6 papers; trusts 32 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 21 citations into it; #5,040 of 21,575 by trust here, #6,873 by citations
• Representation & Self-Supervised Learning — 20 citations into it; #1,026 of 3,145 by trust here, #2,330 by citations
• Artificial Intelligence — 15 citations into it; #14,509 of 35,345 by trust here, #12,387 by citations
• Statistical Learning Theory — 8 citations into it; #399 of 1,038 by trust here, #678 by citations
• Computational Mechanics — 7 citations into it

Whom Daniel D. Lee trusts overall

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

1. Sam T. Roweis
2. Christopher K. I. Williams
3. Lawrence K. Saul
4. I. J. Schoenberg
5. Bernard Haasdonk
6. Ian T. Jolliffe
7. A. P. Dawid
8. Risi Kondor
9. Matthew A. Turk
10. Bastian Leibe

Who trusts Daniel D. Lee overall

1. Mohammad Rostami
2. Mehrtash Harandi
3. Chang Wang
4. Zhiwu Huang
5. Sridhar Mahadevan
6. Sherif Azary
7. Stephan K. Chalup
8. Byron Boots
9. Raghuraman Gopalan
10. Shiming Xiang

Papers from Daniel D. Lee's perspective

Ordered by how strongly the researchers Daniel D. Lee 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• A kernel view of the dimensionality reduction of manifolds (2004) — 632 citations
• Grassmann discriminant analysis: a unifying view on subspace-based learning (2008) — 686 citations
• Spectral Methods for Dimensionality Reduction (2006) — 296 citations
• Causal reasoning in a prediction task with hidden causes (2015)
• Semisupervised alignment of manifolds (2005) — 313 citations
• Learning High Dimensional Correspondences from Low Dimensional Manifolds (2003) — 92 citations