Melba M. Crawford

Computer Vision and Pattern Recognition · 2005–2014

A researcher in the Science corpus: 4 papers; trusts 33 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 35 citations into it; #11,301 of 21,575 by trust here, #16,787 by citations
• Media Technology — 30 citations into it; #753 of 3,621 by trust here, #494 by citations
• Artificial Intelligence — 22 citations into it; #20,033 of 35,345 by trust here, #11,490 by citations
• Atmospheric Science — 15 citations into it; #1,614 of 4,804 by trust here, #2,243 by citations
• Analytical Chemistry — 14 citations into it; #396 of 1,254 by trust here, #181 by citations
• Representation & Self-Supervised Learning — 14 citations into it
• Ecology — 9 citations into it
• Computational Theory and Mathematics — 6 citations into it

Whom Melba M. Crawford trusts overall

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

1. Christopher K. I. Williams
2. Yoshua Bengio
3. Sam T. Roweis
4. Leo Breiman
5. Mikhail Belkin
6. Josh Tenenbaum
7. Geoffrey E. Hinton
8. Nirmal Keshava
9. Thomas Kailath
10. Lawrence K. Saul

Who trusts Melba M. Crawford overall

1. Wei Li
2. Gong Cheng
3. Devis Tuia
4. Yi Chen
5. Xiuping Jia
6. Renlong Hang
7. Jonathan Cheung-Wai Chan

Papers from Melba M. Crawford's perspective

Ordered by how strongly the researchers Melba M. Crawford 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Investigation of the random forest framework for classification of hyperspectral data (2005) — 1,210 citations
• Manifold-Learning-Based Feature Extraction for Classification of Hyperspectral Data: A Review of Advances in Manifold Learning (2014) — 292 citations
• Feature Mining for Hyperspectral Image Classification (2013) — 370 citations
• Local Manifold Learning-Based $k$ -Nearest-Neighbor for Hyperspectral Image Classification (2010) — 547 citations