Michael A. Chapman

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 194 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 82 citations into it
• Computational Mechanics — 44 citations into it
• Aerospace Engineering — 27 citations into it
• Environmental Engineering — 27 citations into it
• Media Technology — 17 citations into it; #2,203 of 3,621 by trust here, #1,498 by citations
• 3D Vision & Pose — 16 citations into it
• Artificial Intelligence — 14 citations into it
• Atmospheric Science — 12 citations into it; #2,551 of 4,804 by trust here, #2,243 by citations

Whom Michael A. Chapman trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Sergey Ioffe
5. Karen Simonyan
6. Christian Szegedy
7. Yann LeCun
8. Ilya Sutskever
9. Yoshua Bengio
10. Ross B. Girshick

Who trusts Michael A. Chapman overall

1. Swalpa Kumar Roy

Papers from Michael A. Chapman's perspective

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

Their papers

• Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework (2018) — 1,589 citations
• Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review (2020) — 547 citations