Yucen Luo

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 348 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 92 citations into it; #11,482 of 35,345 by trust here, #10,667 by citations
• Computer Vision and Pattern Recognition — 74 citations into it; #7,731 of 21,575 by trust here, #7,499 by citations
• Representation & Self-Supervised Learning — 50 citations into it; #2,014 of 3,145 by trust here, #1,943 by citations
• Probabilistic & Bayesian Methods — 37 citations into it
• Generative Models — 26 citations into it
• Optimization & Training — 18 citations into it
• Statistics and Probability — 11 citations into it
• Model Architectures — 7 citations into it

Whom Yucen Luo trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. L. Eon Bottou
7. Radford M. Neal
8. Kaiming He
9. Ilya Sutskever
10. Tim Salimans

Who trusts Yucen Luo overall

1. Lu Liu
2. Hui Tang
3. Jian Liang
4. Chongxuan Li
5. Zhijie Deng
6. Dapeng Hu
7. K. Jia
8. Yanbei Chen
9. Robby T. Tan
10. Paola Cascante-Bonilla

Papers from Yucen Luo's perspective

Ordered by how strongly the researchers Yucen Luo 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
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

• Smooth Neighbors on Teacher Graphs for Semi-Supervised Learning (2017) — 262 citations
• Cluster Alignment With a Teacher for Unsupervised Domain Adaptation (2019) — 246 citations
• ZhuSuan: A Library for Bayesian Deep Learning (2017) — 43 citations