William Grant Hatcher

Computer Networks and Communications · 2018

A researcher in the Science corpus: 2 papers; trusts 63 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Networks and Communications — 44 citations into it; #9,289 of 9,886 by trust here, #3,753 by citations
• Artificial Intelligence — 37 citations into it
• Computer Vision and Pattern Recognition — 32 citations into it
• Electrical and Electronic Engineering — 27 citations into it
• Signal Processing — 9 citations into it
• Reinforcement Learning — 7 citations into it
• Economics and Econometrics — 5 citations into it
• Management Science and Operations Research — 5 citations into it

Whom William Grant Hatcher trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Ilya Sutskever
6. Aliaksei Severyn
7. Yann LeCun
8. Diederik P. Kingma
9. David Silver
10. X. Zhang

Who trusts William Grant Hatcher overall

1. Oludare Isaac Abiodun
2. Weiwei Jiang
3. Aman Bin Jantan
4. Stefano Savazzi

Papers from William Grant Hatcher's perspective

Ordered by how strongly the researchers William Grant Hatcher 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
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

• A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends (2018) — 533 citations
• A Survey on the Edge Computing for the Internet of Things (2018) — 1,262 citations