Frederick Tung

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 7 papers; trusts 1,089 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 210 citations into it; #6,040 of 21,575 by trust here, #5,987 by citations
• Artificial Intelligence — 158 citations into it; #8,444 of 35,345 by trust here, #8,365 by citations
• Efficiency & Compression — 99 citations into it; #169 of 1,264 by trust here, #633 by citations
• Model Architectures — 35 citations into it; #2,254 of 2,278 by trust here, #1,826 by citations
• Generative Models — 25 citations into it; #1,131 of 1,956 by trust here, #1,021 by citations
• Optimization & Training — 24 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Computational Mechanics — 13 citations into it

Whom Frederick Tung trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Kaiming He
4. Yann LeCun
5. Karen Simonyan
6. Ilya Sutskever
7. Yoshua Bengio
8. Andrew Zisserman
9. Diederik P. Kingma
10. X. Zhang

Who trusts Frederick Tung overall

1. Yang He
2. Mingi Ji
3. Greg Mori
4. Mengyao Zhai
5. Elahe Arani
6. Fahad Sarfraz
7. Lin Wang
8. Umberto Michieli
9. Zhicheng Sun
10. Hanqiu Deng

Papers from Frederick Tung's perspective

Ordered by how strongly the researchers Frederick Tung 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• CLIP-Q: Deep Network Compression Learning by In-parallel Pruning-Quantization (2018) — 223 citations
• Lifelong GAN: Continual Learning for Conditional Image Generation (2019) — 210 citations
• Multi-level Residual Networks from Dynamical Systems View (2017) — 184 citations
• Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation (2021) — 32 citations
• Similarity-Preserving Knowledge Distillation (2019) — 1,259 citations
• Constraint-Aware Deep Neural Network Compression (2018) — 75 citations
• Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization (2017) — 37 citations