Xiaomeng Li
Computer Vision and Pattern Recognition · 2018–2019
A researcher in the Science corpus: 6 papers; trusts 138 researchers and is trusted by 5 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 80 citations into it; #12,776 of 21,575 by trust here, #5,053 by citations
- Artificial Intelligence — 56 citations into it; #20,542 of 35,345 by trust here, #7,010 by citations
- Model Architectures — 17 citations into it
- Representation & Self-Supervised Learning — 10 citations into it
- Biophysics — 7 citations into it
- Optimization & Training — 7 citations into it
- Media Technology — 6 citations into it
Whom Xiaomeng Li trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Gao Huang
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yoshua Bengio
- Yann LeCun
- X. Zhang
- Ilya Sutskever
- Ross B. Girshick
Who trusts Xiaomeng Li overall
- Xiangde Luo
- Rushi Jiao
- Jieneng Chen
- Kaiwen Zhang
- Tao Li
- Qikui Zhu
- Daiqing Li
- Ali Hatamizadeh
- Tongle Fan
- Yichi Zhang
Papers from Xiaomeng Li's perspective
Ordered by how strongly the researchers Xiaomeng Li trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- The Liver Tumor Segmentation Benchmark (LiTS) (2019) — 1,500 citations
- H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes (2018) — 2,217 citations
- Transformation-Consistent Self-Ensembling Model for Semisupervised Medical Image Segmentation (2019) — 495 citations
- Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation (2019) — 1,259 citations
- Difficulty-aware Meta-Learning for Rare Disease Diagnosis (2019) — 75 citations
- CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy and Diabetic Macular Edema Grading (2019) — 367 citations