Fu Jie Huang

Computer Vision and Pattern Recognition · 2004–2008

A researcher in the Science corpus: 7 papers; trusts 329 researchers and is trusted by 6,110 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 72 citations into it; #330 of 21,575 by trust here, #1,264 by citations
• Artificial Intelligence — 28 citations into it; #5,287 of 35,345 by trust here, #16,166 by citations
• Optimization & Training — 14 citations into it
• Natural Language Processing — 11 citations into it
• Aerospace Engineering — 9 citations into it
• Model Architectures — 9 citations into it
• Media Technology — 8 citations into it; #211 of 3,621 by trust here, #143 by citations
• Probabilistic & Bayesian Methods — 7 citations into it; #359 of 1,525 by trust here, #166 by citations

Whom Fu Jie Huang trusts overall

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

1. Yann LeCun
2. L. Eon Bottou
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. David G. Lowe
6. Yuhai Wu
7. C. Burges
8. John C. Platt
9. Alex Krizhevsky
10. Marc'Aurelio Ranzato

Who trusts Fu Jie Huang overall

1. Yoshua Bengio
2. Yann LeCun
3. Ruslan Salakhutdinov
4. Quoc V. Le
5. Marc'Aurelio Ranzato
6. J. Urgen Schmidhuber
7. Geoffrey E. Hinton
8. Sergey Levine
9. Trevor Darrell
10. Kristen Grauman

Papers from Fu Jie Huang's perspective

Ordered by how strongly the researchers Fu Jie Huang 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. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition (2007) — 1,191 citations
• Learning methods for generic object recognition with invariance to pose and lighting (2004) — 1,593 citations
• Large-scale Learning with SVM and Convolutional for Generic Object Categorization (2006) — 386 citations
• Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition (2008)
• Energy-Based Models in Document Recognition and Computer Vision (2007) — 62 citations
• A Tutorial on Energy-Based Learning (2006) — 1,869 citations
• Loss Functions for Discriminative Training of Energy-Based Models (2005) — 163 citations