Chuan-Sheng Foo
Artificial Intelligence · 2007–2021
A researcher in the Science corpus: 8 papers; trusts 142 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 94 citations into it; #14,150 of 35,345 by trust here, #13,962 by citations
- Computer Vision and Pattern Recognition — 57 citations into it; #12,461 of 21,575 by trust here, #7,852 by citations
- Generative Models — 52 citations into it; #1,508 of 1,956 by trust here, #1,221 by citations
- Optimization & Training — 38 citations into it
- Molecular Biology — 26 citations into it; #11,890 of 17,773 by trust here, #15,203 by citations
- Signal Processing — 23 citations into it
- Computational Mechanics — 16 citations into it; #5,537 of 10,001 by trust here, #4,315 by citations
- Management Science and Operations Research — 15 citations into it; #6,888 of 11,946 by trust here, #9,931 by citations
Whom Chuan-Sheng Foo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Alex Krizhevsky
- Geoffrey E. Hinton
- Diederik P. Kingma
- Ian J. Goodfellow
- Yann LeCun
- Alec Radford
- L. Eon Bottou
- Kaiming He
- Ilya Sutskever
Who trusts Chuan-Sheng Foo overall
- Fabian Pedregosa
- Guansong Pang
- Animesh Karnewar
- Samet Akcay
- Stephen Gould
- H. Brendan McMahan
- Kegan G. G. Samuel
- Chunhua Shen
- Basura Fernando
- Martin Wistuba
Papers from Chuan-Sheng Foo's perspective
Ordered by how strongly the researchers Chuan-Sheng Foo trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- The Unusual Effectiveness of Averaging in GAN Training (2018) — 183 citations
- Efficient GAN-Based Anomaly Detection (2018) — 607 citations
- Proximal regularization for online and batch learning (2009) — 56 citations
- Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile (2018) — 342 citations
- Adversarially Learned Anomaly Detection (2018) — 460 citations
- An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series (2021) — 337 citations
- Large-scale whole-genome sequencing of three diverse Asian populations in Singapore (2018) — 153 citations
- Efficient multiple hyperparameter learning for log-linear models (2007) — 120 citations