Jianru Xue

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 4 papers; trusts 426 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 167 citations into it; #10,995 of 21,575 by trust here, #8,205 by citations
• Biomedical Engineering — 82 citations into it; #3,321 of 10,734 by trust here, #4,405 by citations
• Artificial Intelligence — 76 citations into it
• Human-Computer Interaction — 44 citations into it; #839 of 2,253 by trust here, #503 by citations
• Model Architectures — 31 citations into it; #1,617 of 2,278 by trust here, #1,490 by citations
• 3D Vision & Pose — 18 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Optimization & Training — 8 citations into it

Whom Jianru Xue trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Sepp Hochreiter
6. Yoshua Bengio
7. Ilya Sutskever
8. Ross B. Girshick
9. Yann LeCun
10. J. Urgen Schmidhuber

Who trusts Jianru Xue overall

1. Yisheng Song

Papers from Jianru Xue's perspective

Ordered by how strongly the researchers Jianru Xue 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction (2019) — 548 citations
• View Adaptive Recurrent Neural Networks for High Performance Human Action Recognition from Skeleton Data (2017) — 619 citations
• View Adaptive Neural Networks for High Performance Skeleton-Based Human Action Recognition (2018) — 490 citations
• Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition (2019) — 543 citations