Ting-Kam Leonard Wong

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 20 citations into it
• Finance — 13 citations into it; #9,936 of 10,518 by trust here, #10,352 by citations
• Statistical and Nonlinear Physics — 13 citations into it; #8,543 of 10,127 by trust here, #5,200 by citations
• Model Architectures — 10 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Optimization & Training — 7 citations into it; #2,403 of 2,601 by trust here, #1,904 by citations
• Generative Models — 6 citations into it
• Management Science and Operations Research — 6 citations into it

Whom Ting-Kam Leonard Wong trusts overall

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

1. Shun-ichi Amari
2. Diederik P. Kingma
3. Joel A. E. Andersson
4. Eldad Haber
5. Barak A. Pearlmutter
6. Lars Ruthotto
7. W. E
8. Bernt Øksendal
9. Hiroshi Kunita
10. Robert Fernholz

Who trusts Ting-Kam Leonard Wong overall

1. Qinsheng Zhang
2. Yang Song
3. Chin-Wei Huang
4. Jacob Kelly
5. Liam Hodgkinson
6. Conor Durkan

Papers from Ting-Kam Leonard Wong's perspective

Ordered by how strongly the researchers Ting-Kam Leonard Wong trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• The geometry of relative arbitrage (2014) — 59 citations
• Scalable Gradients for Stochastic Differential Equations (2020) — 434 citations