Jiakai Zhang

Computer Vision and Pattern Recognition · 2016–2017

A researcher in the Science corpus: 5 papers; trusts 68 researchers and is trusted by 32 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 41 citations into it; #5,865 of 21,575 by trust here, #1,788 by citations
• Artificial Intelligence — 31 citations into it; #6,448 of 35,345 by trust here, #2,368 by citations
• Generative Models — 31 citations into it
• Reinforcement Learning — 16 citations into it; #585 of 2,242 by trust here, #227 by citations
• Optimization & Training — 12 citations into it
• Management Science and Operations Research — 8 citations into it
• Statistical and Nonlinear Physics — 8 citations into it

Whom Jiakai Zhang trusts overall

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

1. Stéphane Ross
2. Geoffrey E. Hinton
3. Yann LeCun
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ilya Sutskever
7. Wojciech Zaremba
8. Jan Koutník
9. Kai-Wei Chang
10. J. Urgen Schmidhuber

Who trusts Jiakai Zhang overall

1. Trevor Ablett
2. Yunpeng Pan
3. Yunzhu Li
4. Philip Bachman
5. Filip Mari'c
6. Alexander Lin
7. Li Chen
8. Ching-An Cheng
9. Eric Zhan
10. B. R. Kiran

Papers from Jiakai Zhang's perspective

Ordered by how strongly the researchers Jiakai Zhang 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. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• End to End Learning for Self-Driving Cars (2016) — 4,658 citations
• Query-Efficient Imitation Learning for End-to-End Simulated Driving (2017) — 129 citations
• Query-Efficient Imitation Learning for End-to-End Autonomous Driving (2016) — 238 citations
• Towards the Automatic Anime Characters Creation with Generative Adversarial Networks (2017) — 202 citations
• Towards the High-quality Anime Characters Generation with Generative Adversarial Networks (2017)