Harold Christopher Burger

Computer Vision and Pattern Recognition · 2012

A researcher in the Science corpus: 2 papers; trusts 101 researchers and is trusted by 299 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 27 citations into it; #2,305 of 21,575 by trust here, #5,116 by citations
• Media Technology — 11 citations into it; #247 of 3,621 by trust here, #353 by citations
• Artificial Intelligence — 10 citations into it
• Computational Mechanics — 10 citations into it
• Optimization & Training — 6 citations into it; #2,285 of 2,601 by trust here, #2,484 by citations
• Probabilistic & Bayesian Methods — 5 citations into it
• Representation & Self-Supervised Learning — 5 citations into it
• Signal Processing — 5 citations into it

Whom Harold Christopher Burger trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Yoshua Bengio
4. Marc'Aurelio Ranzato
5. Koray Kavukcuoglu
6. Dumitru Erhan
7. Alex Krizhevsky
8. L. Eon Bottou
9. Jianguo Zhang
10. Pascal Vincent

Who trusts Harold Christopher Burger overall

1. Kyunghyun Cho
2. Kai Zhang
3. Qifeng Chen
4. Chunwei Tian
5. Wangmeng Zuo
6. Stamatios Lefkimmiatis
7. Lovedeep Gondara
8. Hang Zhao
9. X. Wang
10. Lei Zhang

Papers from Harold Christopher Burger's perspective

Ordered by how strongly the researchers Harold Christopher Burger 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Image denoising: Can plain neural networks compete with BM3D? (2012) — 1,345 citations
• Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms (2012) — 34 citations