Simon Tong

Artificial Intelligence · with Zhou · 2000–2021

A researcher in the Science corpus: 3 papers; trusts 7 researchers and is trusted by 205 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 82 citations into it; #4,908 of 35,345 by trust here, #5,600 by citations
• Natural Language Processing — 63 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it; #3,446 of 21,575 by trust here, #4,152 by citations
• Optimization & Training — 8 citations into it

Whom Simon Tong trusts overall

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

1. Thorsten Joachims
2. David D. Lewis
3. Yuhai Wu
4. H. Sebastian Seung
5. Fabrizio Sebastiani
6. Andrew McCallum
7. Bernhard Schölkopf
8. Jason Catlett
9. Manfred Opper
10. Constantine Papageorgiou

Who trusts Simon Tong overall

1. Burr Settles
2. Matthias W. Seeger
3. Gang Wu
4. Hieu Tat Nguyen
5. Edward Y. Chang
6. Thomas G. Dietterich
7. Hema Raghavan
8. Dacheng Tao
9. Ritendra Datta
10. Yi Yang

Papers from Simon Tong's perspective

Ordered by how strongly the researchers Simon Tong 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• GLaM: Efficient Scaling of Language Models with Mixture-of-Experts (2021) — 1,261 citations
• Support vector machine active learning for image retrieval (2001) — 1,552 citations
• Support Vector Machine Active Learning with Applications to Text Classification (2000) — 3,523 citations