Kaixiang Lin

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 12 papers; trusts 381 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 220 citations into it; #18,541 of 35,345 by trust here, #20,377 by citations
• Reinforcement Learning — 157 citations into it
• Electrical and Electronic Engineering — 71 citations into it
• Computer Vision and Pattern Recognition — 69 citations into it
• Optimization & Training — 31 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Computational Mechanics — 30 citations into it; #8,451 of 10,001 by trust here, #7,173 by citations
• Control and Systems Engineering — 30 citations into it

Whom Kaixiang Lin trusts overall

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

1. Richard S. Sutton
2. Geoffrey E. Hinton
3. Ronald J. Williams
4. Radford M. Neal
5. Yoshua Bengio
6. Alex Krizhevsky
7. Diederik P. Kingma
8. Volodymyr Mnih
9. David Silver
10. Yann LeCun

Who trusts Kaixiang Lin overall

1. Yu Zhang
2. Qiang Yang
3. Daochen Zha
4. Thanh Thi Nguyen
5. Sergey I. Nikolenko
6. Zhuangdi Zhu
7. Caglar Gulcehre
8. Xi Yin
9. Lorenzo Canese
10. Aleksei Triastcyn

Papers from Kaixiang Lin's perspective

Ordered by how strongly the researchers Kaixiang 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. 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

• Transfer Learning in Deep Reinforcement Learning: A Survey (2020) — 899 citations
• Alkaline quinone flow battery (2015) — 881 citations
• Uniform High Ionic Conducting Lithium Sulfide Protection Layer for Stable Lithium Metal Anode (2019) — 369 citations
• Alkaline Quinone Flow Battery with Long Lifetime at pH 12 (2018) — 359 citations
• Ranking Policy Gradient (2019) — 8 citations
• Differentially Private Generative Adversarial Network (2018) — 593 citations
• Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning (2018) — 386 citations
• Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulation (2018) — 9 citations
• Privacy-Preserving Distributed Multi-Task Learning with Asynchronous Updates (2017) — 77 citations
• Interactive Multi-task Relationship Learning (2016) — 12 citations
• Multi-Task Feature Interaction Learning (2016) — 33 citations
• A redox-flow battery with an alloxazine-based organic electrolyte (2016) — 461 citations