Horace Pan

Computer Vision and Pattern Recognition · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 19 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 30 citations into it
• Graph Learning — 29 citations into it; #215 of 693 by trust here, #279 by citations
• Artificial Intelligence — 27 citations into it; #9,488 of 35,345 by trust here, #12,083 by citations
• Optimization & Training — 5 citations into it

Whom Horace Pan trusts overall

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

1. Karsten M. Borgwardt
2. Risi Kondor
3. Bernhard Schölkopf
4. Tony Jebara
5. Christopher K. I. Williams
6. Alex Smola
7. S. V. N. Vishwanathan
8. Nino Shervashidze
9. Fredrik D. Johansson
10. Geoffrey E. Hinton

Who trusts Horace Pan overall

1. Saurabh Verma
2. Risi Kondor
3. Christopher Morris
4. Nils M. Kriege
5. Zhi-Li Zhang
6. Fan-Yun Sun
7. Giannis Nikolentzos
8. Yujun Yan
9. Haggai Maron
10. Zhengdao Chen

Papers from Horace Pan's perspective

Ordered by how strongly the researchers Horace Pan 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Covariant Compositional Networks For Learning Graphs (2018) — 168 citations
• Predicting molecular properties with covariant compositional networks. (2018) — 73 citations
• The Multiscale Laplacian Graph Kernel (2016) — 187 citations