Bingsheng He

Artificial Intelligence · Computer Vision and Pattern Recognition · 2008–2021

A researcher in the Science corpus: 10 papers; trusts 373 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 293 citations into it; #13,249 of 35,345 by trust here, #8,210 by citations
• Federated & Distributed Learning — 111 citations into it
• Computer Vision and Pattern Recognition — 91 citations into it
• Computer Networks and Communications — 62 citations into it
• Optimization & Training — 54 citations into it
• Privacy & Security — 52 citations into it
• Electrical and Electronic Engineering — 36 citations into it
• Hardware and Architecture — 31 citations into it

Whom Bingsheng He trusts overall

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

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

Who trusts Bingsheng He overall

1. Qinbin Li
2. Mang Ye
3. Junzhe Zhang
4. Wenke Huang
5. Shuai Che
6. Adwait Jog
7. Alex D. Breslow
8. Lizhe Wang
9. Haicheng Wu
10. Christopher J. Rossbach

Papers from Bingsheng He's perspective

Ordered by how strongly the researchers Bingsheng He 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Mars: A MapReduce Framework on graphics processors (2008) — 838 citations
• Practical Federated Gradient Boosting Decision Trees (2019) — 220 citations
• Model-Contrastive Federated Learning (2021) — 1,746 citations
• Federated Learning on Non-IID Data Silos: An Experimental Study (2021) — 1,460 citations
• Efficient Memory Management for GPU-based Deep Learning Systems (2019) — 45 citations
• A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection (2019) — 1,461 citations
• Layer-Centric Memory Reuse and Data Migration for Extreme-Scale Deep Learning on Many-Core Architectures (2018) — 39 citations
• NV-Tree: Reducing Consistency Cost for NVM-based Single Level Systems (2015) — 346 citations
• Relational query coprocessing on graphics processors (2009) — 307 citations
• Relational joins on graphics processors (2008) — 392 citations