Waseem Rawat

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 1 paper; trusts 1,046 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 163 citations into it; #10,955 of 21,575 by trust here, #8,612 by citations
• Artificial Intelligence — 136 citations into it; #17,350 of 35,345 by trust here, #12,387 by citations
• Model Architectures — 48 citations into it
• Optimization & Training — 42 citations into it
• Representation & Self-Supervised Learning — 29 citations into it
• Efficiency & Compression — 21 citations into it
• Signal Processing — 16 citations into it
• Safety, Robustness & Interpretability — 14 citations into it

Whom Waseem Rawat trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Alex Krizhevsky
4. Yoshua Bengio
5. Kaiming He
6. L. Eon Bottou
7. Ilya Sutskever
8. Diederik P. Kingma
9. J. Urgen Schmidhuber
10. Karen Simonyan

Who trusts Waseem Rawat overall

1. Grace W. Lindsay
2. Fred Hohman
3. Sweta Bhattacharya
4. Deboleena Roy
5. Weiwei Jiang
6. Minsuk Kahng
7. Yu Jiang
8. Praveen Kumar Reddy Maddikunta
9. Priyadarshini Panda
10. Yongquan Yang

Papers from Waseem Rawat's perspective

Ordered by how strongly the researchers Waseem Rawat 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review (2017) — 3,214 citations