Jin-Siang Lin

Artificial Intelligence · 2021

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 18 citations into it
• Reinforcement Learning — 12 citations into it

Whom Jin-Siang Lin trusts overall

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

1. John Schulman
2. Anirudh Goyal
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Sergey Levine
6. Diederik P. Kingma
7. Pieter Abbeel
8. Richard S. Sutton
9. Pierre-Luc Bacon
10. Alex Krizhevsky

Papers from Jin-Siang Lin's perspective

Ordered by how strongly the researchers Jin-Siang 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Toward Robust Long Range Policy Transfer (2021) — 8 citations