Kun Dong

Probabilistic & Bayesian Methods · 2017

A researcher in the Science corpus: 1 paper; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Probabilistic & Bayesian Methods — 9 citations into it
• Artificial Intelligence — 8 citations into it
• Statistics and Probability — 6 citations into it
• Computational Theory and Mathematics — 5 citations into it

Whom Kun Dong trusts overall

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

1. Andrew Gordon Wilson
2. Carl E. Rasmussen
3. Temple F. Smith
4. Michael F. Hutchinson
5. Edward Snelson
6. Joaquin Quiñonero Candela
7. Quoc V. Le
8. Hannes Nickisch
9. Gene H. Golub
10. James Hensman

Who trusts Kun Dong overall

1. Jacob V. Gardner
2. Insu Han
3. David Eriksson
4. Haim Avron

Papers from Kun Dong's perspective

Ordered by how strongly the researchers Kun Dong 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Scalable Log Determinants for Gaussian Process Kernel Learning (2017) — 109 citations