Dapeng Hu

Artificial Intelligence · with Feng · 2020–2021

A researcher in the Science corpus: 7 papers; trusts 1,166 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 322 citations into it; #9,118 of 35,345 by trust here, #11,192 by citations
• Computer Vision and Pattern Recognition — 278 citations into it; #6,422 of 21,575 by trust here, #8,409 by citations
• Representation & Self-Supervised Learning — 180 citations into it; #1,457 of 3,145 by trust here, #1,843 by citations
• Radiology, Nuclear Medicine and Imaging — 55 citations into it; #639 of 3,265 by trust here, #1,355 by citations
• Optimization & Training — 33 citations into it
• Cancer Research — 25 citations into it
• Federated & Distributed Learning — 22 citations into it
• Evaluation & Benchmarks — 19 citations into it

Whom Dapeng Hu trusts overall

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

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

Who trusts Dapeng Hu overall

1. Hongbin Lin
2. Shuaicheng Niu
3. Yifan Zhang
4. Jian Liang
5. Junyuan Hong
6. Dequan Wang
7. Yusuke Iwasawa
8. Liyuan Wang
9. Zhen Qiu
10. Yuxi Wang

Papers from Dapeng Hu's perspective

Ordered by how strongly the researchers Dapeng Hu 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation (2020) — 1,724 citations
• How Well Does Self-Supervised Pre-Training Perform with Streaming Data? (2021) — 39 citations
• A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation (2020) — 123 citations
• Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning (2021) — 81 citations
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data (2021) — 483 citations
• Adversarial Domain Adaptation With Prototype-Based Normalized Output Conditioner (2020) — 38 citations
• Source Data-Absent Unsupervised Domain Adaptation Through Hypothesis Transfer and Labeling Transfer (2020) — 358 citations