Matthew Tang

Computer Vision and Pattern Recognition · 2017

A researcher in the Science corpus: 1 paper; trusts 14 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 21 citations into it; #9,005 of 21,575 by trust here, #3,875 by citations
• Artificial Intelligence — 16 citations into it
• Model Architectures — 6 citations into it

Whom Matthew Tang trusts overall

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

1. Christian Szegedy
2. Alex Krizhevsky
3. Kaiming He
4. Sergey Ioffe
5. Geoffrey E. Hinton

Who trusts Matthew Tang overall

1. Markus Nagel
2. Ji Lin
3. Zhen Dong
4. Young Geun Kim

Papers from Matthew Tang's perspective

Ordered by how strongly the researchers Matthew Tang 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. ImageNet: A large-scale hierarchical image database
4. Going deeper with convolutions
5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Microsoft COCO: Common Objects in Context
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Rethinking the Inception Architecture for Computer Vision

Their papers

• Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference (2017) — 4,475 citations