Ting-Bing Xu
Computer Vision and Pattern Recognition · 2019
A researcher in the Science corpus: 1 paper; trusts 233 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 32 citations into it; #7,272 of 21,575 by trust here, #17,757 by citations
- Artificial Intelligence — 25 citations into it; #10,681 of 35,345 by trust here, #27,551 by citations
- Efficiency & Compression — 12 citations into it; #398 of 1,264 by trust here, #1,160 by citations
- Model Architectures — 9 citations into it
- Representation & Self-Supervised Learning — 9 citations into it; #1,253 of 3,145 by trust here, #2,805 by citations
- Optimization & Training — 6 citations into it
- Signal Processing — 5 citations into it
Whom Ting-Bing Xu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Yann LeCun
- Geoffrey E. Hinton
- Alex Krizhevsky
- X. Zhang
- Ilya Sutskever
- Shaoqing Ren
- Yoshua Bengio
- Individualized Treat
- Ross B. Girshick
Who trusts Ting-Bing Xu overall
- Jianping Gou
- Lin Wang
- Liyuan Sun
- Sukmin Yun
- Mikhail Khodak
- Mingi Ji
- Chuanguang Yang
- Kuk-Jin Yoon
- Dacheng Tao
- Baosheng Yu
Papers from Ting-Bing Xu's perspective
Ordered by how strongly the researchers Ting-Bing Xu trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Data-Distortion Guided Self-Distillation for Deep Neural Networks (2019) — 207 citations