Debbie Marr

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 4 papers; trusts 256 researchers and is trusted by 33 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 63 citations into it; #4,894 of 21,575 by trust here, #6,326 by citations
• Artificial Intelligence — 61 citations into it; #6,334 of 35,345 by trust here, #8,893 by citations
• Efficiency & Compression — 38 citations into it
• Model Architectures — 20 citations into it
• Optimization & Training — 13 citations into it
• ML Systems & Hardware — 6 citations into it

Whom Debbie Marr trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Matthieu Courbariaux
5. Kaiming He
6. Yann LeCun
7. Ilya Sutskever
8. Christian Szegedy
9. Diederik P. Kingma
10. X. Zhang

Who trusts Debbie Marr overall

1. Sungho Shin
2. Qing Jin
3. Lin Wang
4. Yoonho Boo
5. Jianping Gou
6. Chaim Baskin
7. Tejalal Choudhary
8. Aojun Zhou
9. Ancong Wu
10. Jangho Kim

Papers from Debbie Marr's perspective

Ordered by how strongly the researchers Debbie Marr 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. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Going deeper with convolutions

Their papers

• Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy (2017) — 354 citations
• WRPN: Wide Reduced-Precision Networks (2017) — 278 citations
• Can FPGAs Beat GPUs in Accelerating Next-Generation Deep Neural Networks? (2017) — 456 citations
• Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC (2016) — 318 citations