Pingbo Pan

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 403 researchers and is trusted by 81 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 68 citations into it; #3,613 of 21,575 by trust here, #4,623 by citations
• Artificial Intelligence — 58 citations into it; #25,087 of 35,345 by trust here, #15,236 by citations
• Model Architectures — 17 citations into it
• Natural Language Processing — 17 citations into it
• Generative Models — 16 citations into it; #533 of 1,956 by trust here, #759 by citations
• Optimization & Training — 15 citations into it
• Evaluation & Benchmarks — 11 citations into it
• Multimodal Learning — 9 citations into it

Whom Pingbo Pan trusts overall

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

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

Who trusts Pingbo Pan overall

1. Fei Ye
2. Shaoxiang Chen
3. Linchao Zhu
4. Yunchen Pu
5. Adrian G. Bors
6. X. Wang
7. Ramakanth Pasunuru
8. Zhongwen Xu
9. Jaemin Cho
10. Mohit Bansal

Papers from Pingbo Pan's perspective

Ordered by how strongly the researchers Pingbo Pan 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-To-Image Synthesis (2019) — 672 citations
• Hierarchical Recurrent Neural Encoder for Video Representation with Application to Captioning (2015) — 397 citations
• Continual Deep Learning by Functional Regularisation of Memorable Past (2020) — 174 citations