Yuanpu Xie

Computer Vision and Pattern Recognition · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 662 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 124 citations into it; #5,456 of 21,575 by trust here, #5,565 by citations
• Artificial Intelligence — 70 citations into it; #20,508 of 35,345 by trust here, #9,760 by citations
• Generative Models — 30 citations into it; #613 of 1,956 by trust here, #1,021 by citations
• Model Architectures — 25 citations into it
• Biophysics — 16 citations into it
• Multimodal Learning — 15 citations into it; #331 of 1,211 by trust here, #722 by citations
• Natural Language Processing — 12 citations into it
• Media Technology — 10 citations into it

Whom Yuanpu Xie trusts overall

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

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

Who trusts Yuanpu Xie overall

1. Zizhao Zhang
2. Tobias Hinz
3. Alfredo Vellido
4. Lin F. Yang
5. Mingkuan Yuan
6. Animesh Karnewar
7. Fuyong Xing
8. Tingting Qiao
9. Jinhyeok Yang
10. Hongchen Tan

Papers from Yuanpu Xie's perspective

Ordered by how strongly the researchers Yuanpu Xie trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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

• Photographic Text-to-Image Synthesis with a Hierarchically-Nested Adversarial Network (2018) — 323 citations
• MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network (2017) — 343 citations
• Pathologist-level interpretable whole-slide cancer diagnosis with deep learning (2019) — 318 citations
• Deep Learning in Microscopy Image Analysis: A Survey (2018) — 392 citations
• An Automatic Learning-Based Framework for Robust Nucleus Segmentation (2016) — 322 citations