Tong Che

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 8 papers; trusts 493 researchers and is trusted by 56 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 171 citations into it; #4,143 of 21,575 by trust here, #3,075 by citations
• Artificial Intelligence — 155 citations into it; #29,183 of 35,345 by trust here, #12,974 by citations
• Generative Models — 114 citations into it; #488 of 1,956 by trust here, #373 by citations
• Probabilistic & Bayesian Methods — 54 citations into it
• Model Architectures — 37 citations into it
• Optimization & Training — 37 citations into it
• Natural Language Processing — 28 citations into it
• Statistical and Nonlinear Physics — 23 citations into it; #4,097 of 10,127 by trust here, #1,059 by citations

Whom Tong Che trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Ronald J. Williams
9. Ruslan Salakhutdinov
10. Alec Radford

Who trusts Tong Che overall

1. Yongjun Hong
2. Aditya Grover
3. Martin Heusel
4. Thomas Unterthiner
5. Sandeep Subramanian
6. Ngoc-Trung Tran
7. Uiwon Hwang
8. Abdul Jabbar
9. Jie Gui
10. William Fedus

Papers from Tong Che's perspective

Ordered by how strongly the researchers Tong Che 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Mode Regularized Generative Adversarial Networks (2016) — 593 citations
• Boundary-Seeking Generative Adversarial Networks (2017) — 176 citations
• Maximum-Likelihood Augmented Discrete Generative Adversarial Networks (2017) — 250 citations
• Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling (2020) — 127 citations
• Residual Connections Encourage Iterative Inference (2017) — 182 citations
• Architectural Complexity Measures of Recurrent Neural Networks (2016) — 164 citations
• Energy-Based Open-World Uncertainty Modeling for Confidence Calibration (2021) — 80 citations
• MetaGAN: An Adversarial Approach to Few-Shot Learning (2018) — 451 citations