Nicholas J. Fraser

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 155 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 64 citations into it; #20,591 of 21,575 by trust here, #10,010 by citations
• Artificial Intelligence — 49 citations into it; #33,740 of 35,345 by trust here, #16,471 by citations
• Efficiency & Compression — 33 citations into it
• Electrical and Electronic Engineering — 21 citations into it; #15,027 of 16,539 by trust here, #9,011 by citations
• Model Architectures — 11 citations into it
• Optimization & Training — 7 citations into it
• Media Technology — 5 citations into it

Whom Nicholas J. Fraser trusts overall

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

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

Who trusts Nicholas J. Fraser overall

1. Sparsh Mittal
2. Bohan Zhuang
3. Haotong Qin
4. Tailin Liang
5. Ahmad Shawahna
6. Josh Fromm

Papers from Nicholas J. Fraser's perspective

Ordered by how strongly the researchers Nicholas J. Fraser 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Attention is All you Need (Neural Information Processing Systems)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks (2018) — 142 citations
• FINN: A Framework for Fast, Scalable Binarized Neural Network Inference (2016) — 1,186 citations
• FINN-R (2018) — 375 citations