Zichuan Lin

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 3 papers; trusts 86 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 72 citations into it
• Reinforcement Learning — 52 citations into it
• Optimization & Training — 19 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it
• Management Science and Operations Research — 11 citations into it
• Control and Systems Engineering — 9 citations into it
• Evaluation & Benchmarks — 6 citations into it
• Meta & Continual Learning — 5 citations into it

Whom Zichuan Lin trusts overall

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

1. Sergey Levine
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Richard S. Sutton
5. Chelsea Finn
6. Diederik P. Kingma
7. Alex Krizhevsky
8. J. Urgen Schmidhuber
9. Ilya Sutskever
10. Yann LeCun

Who trusts Zichuan Lin overall

1. Sae-Young Chung

Papers from Zichuan Lin's perspective

Ordered by how strongly the researchers Zichuan Lin 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Model-based Adversarial Meta-Reinforcement Learning (2020) — 57 citations
• Fully Parameterized Quantile Function for Distributional Reinforcement Learning (2019) — 183 citations
• Deep Q-Networks for Accelerating the Training of Deep Neural Networks (2016) — 12 citations