Christian J. Schuler

Computer Vision and Pattern Recognition · 2011–2014

A researcher in the Science corpus: 6 papers; trusts 289 researchers and is trusted by 485 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 73 citations into it; #2,013 of 21,575 by trust here, #3,489 by citations
• Media Technology — 34 citations into it; #199 of 3,621 by trust here, #259 by citations
• Artificial Intelligence — 21 citations into it
• Computational Mechanics — 18 citations into it
• Optimization & Training — 15 citations into it; #2,284 of 2,601 by trust here, #2,484 by citations
• Probabilistic & Bayesian Methods — 8 citations into it; #274 of 1,525 by trust here, #543 by citations
• Representation & Self-Supervised Learning — 8 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Christian J. Schuler trusts overall

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

1. Geoffrey E. Hinton
2. Yann LeCun
3. Matthew D. Zeiler
4. Yoshua Bengio
5. Alex Krizhevsky
6. Marc'Aurelio Ranzato
7. L. Eon Bottou
8. Anat Levin
9. Diederik P. Kingma
10. Kaiming He

Who trusts Christian J. Schuler overall

1. Zoubin Ghahramani
2. Ayan Chakrabarti
3. Aaron Klein
4. Kai Zhang
5. Kyunghyun Cho
6. Michael T. McCann
7. Tae Hyun Kim
8. Ziyun Wang
9. Bobak Shahriari
10. Wangmeng Zuo

Papers from Christian J. Schuler's perspective

Ordered by how strongly the researchers Christian J. Schuler 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Image denoising: Can plain neural networks compete with BM3D? (2012) — 1,345 citations
• Fast removal of non-uniform camera shake (2011) — 356 citations
• Mask-Specific Inpainting with Deep Neural Networks (2014) — 125 citations
• Learning to Deblur (2014) — 589 citations
• Image denoising with multi-layer perceptrons, part 2: training trade-offs and analysis of their mechanisms (2012) — 34 citations
• Entropy Search for Information-Efficient Global Optimization (2011) — 745 citations