Jonathan Cheung-Wai Chan

Computer Vision and Pattern Recognition · 2008–2018

A researcher in the Science corpus: 3 papers; trusts 71 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 33 citations into it
• Media Technology — 28 citations into it; #861 of 3,621 by trust here, #941 by citations
• Artificial Intelligence — 23 citations into it
• Atmospheric Science — 17 citations into it; #777 of 4,804 by trust here, #1,475 by citations
• Ecology — 10 citations into it; #2,636 of 10,717 by trust here, #5,577 by citations
• Representation & Self-Supervised Learning — 7 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Global and Planetary Change — 5 citations into it

Whom Jonathan Cheung-Wai Chan trusts overall

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

1. Leo Breiman
2. Thomas G. Dietterich
3. J. Ross Quinlan
4. Ron Kohavi
5. Farid Melgani
6. Yoav Freund
7. Eric Bauer
8. Mahesh Pal
9. Saso Dzeroski
10. Trevor J. Hastie

Who trusts Jonathan Cheung-Wai Chan overall

1. Aaron E. Maxwell
2. Joachim Niemeyer
3. Michal Wozniak
4. Michele Dalponte
5. Timothy A. Warner
6. Manuel Graña
7. Franz Rottensteiner
8. Kian Pang Tan
9. Hans Ole Ørka
10. Victor Francisco Rodriguez-Galiano

Papers from Jonathan Cheung-Wai Chan's perspective

Ordered by how strongly the researchers Jonathan Cheung-Wai Chan 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Long Short-Term Memory
8. Going deeper with convolutions
9. Attention is All you Need (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning and Transferring Deep Joint Spectral–Spatial Features for Hyperspectral Classification (2017) — 437 citations
• Random forest regression for online capacity estimation of lithium-ion batteries (2018) — 642 citations
• Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery (2008) — 857 citations