Jun Qi

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 31 citations into it
• Statistical Learning Theory — 6 citations into it
• Statistics and Probability — 5 citations into it

Whom Jun Qi trusts overall

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

1. Peter L. Bartlett
2. Eric R. Ziegel
3. Shai Shalev-Shwartz
4. Shahar Mendelson
5. Richard S. Sutton
6. Cees H. Taal
7. Yong Xu
8. Vladimir Naumovich Vapnik
9. Mahyar Fazlyab
10. Vladimir Naumovich Vapni

Papers from Jun Qi's perspective

Ordered by how strongly the researchers Jun Qi trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
3. Going deeper with convolutions
4. ImageNet classification with deep convolutional neural networks
5. Gradient-based learning applied to document recognition
6. GENERATIVE ADVERSARIAL NETS
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Long Short-Term Memory

Their papers

• Variational Quantum Circuits for Deep Reinforcement Learning (2019) — 454 citations
• On Mean Absolute Error for Deep Neural Network Based Vector-to-Vector Regression (2020) — 299 citations