Hang Qi

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 365 researchers and is trusted by 213 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #3,326 of 35,345 by trust here, #5,834 by citations
• Computer Vision and Pattern Recognition — 68 citations into it; #2,647 of 21,575 by trust here, #4,317 by citations
• Representation & Self-Supervised Learning — 36 citations into it
• Optimization & Training — 22 citations into it; #1,609 of 2,601 by trust here, #1,866 by citations
• Federated & Distributed Learning — 21 citations into it; #64 of 319 by trust here, #158 by citations
• Evaluation & Benchmarks — 13 citations into it
• Model Architectures — 11 citations into it
• Radiology, Nuclear Medicine and Imaging — 9 citations into it; #2,166 of 3,265 by trust here, #2,673 by citations

Whom Hang Qi trusts overall

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

1. Christian Szegedy
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Sergey Ioffe
6. Yann LeCun
7. Xavier Glorot
8. Ross B. Girshick
9. Kaiming He
10. Diederik P. Kingma

Who trusts Hang Qi overall

1. Spyros Gidaris
2. Arthur Douillard
3. Mennatullah Siam
4. Yongqin Xian
5. Fabio Cermelli
6. Nikos Komodakis
7. Matthieu Cord
8. Lucas Caccia
9. Matthew Wallingford
10. Andrei Bursuc

Papers from Hang Qi's perspective

Ordered by how strongly the researchers Hang Qi 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Low-Shot Learning with Imprinted Weights (2017) — 549 citations
• Learning with Imprinted Weights (2017) — 12 citations
• Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification (2019) — 1,651 citations
• Federated Visual Classification with Real-World Data Distribution (2020) — 248 citations
• Paleo: A Performance Model for Deep Neural Networks (2016) — 252 citations