Xuehai Qian

Computer Vision and Pattern Recognition · 2017–2020

A researcher in the Science corpus: 5 papers; trusts 269 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 123 citations into it
• Artificial Intelligence — 104 citations into it
• Electrical and Electronic Engineering — 64 citations into it
• ML Systems & Hardware — 46 citations into it
• Hardware and Architecture — 39 citations into it
• Optimization & Training — 34 citations into it
• Model Architectures — 27 citations into it
• Computer Networks and Communications — 25 citations into it

Whom Xuehai Qian trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Diederik P. Kingma
7. Karen Simonyan
8. Ilya Sutskever
9. Ross B. Girshick
10. Andrew Zisserman

Papers from Xuehai Qian's perspective

Ordered by how strongly the researchers Xuehai Qian 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Capuchin: Tensor-based GPU Memory Management for Deep Learning (2020) — 205 citations
• PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning (2017) — 775 citations
• Heterogeneity-Aware Asynchronous Decentralized Training (2019) — 8 citations
• AccPar: Tensor Partitioning for Heterogeneous Deep Learning Accelerators (2020) — 72 citations
• Hop: Heterogeneity-aware Decentralized Training (2019) — 61 citations