Tianjian Chen

Artificial Intelligence · with Liu · 2018–2022

A researcher in the Science corpus: 16 papers; trusts 141 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 343 citations into it; #8,752 of 35,345 by trust here, #3,523 by citations
• Privacy & Security — 139 citations into it; #332 of 709 by trust here, #243 by citations
• Federated & Distributed Learning — 86 citations into it; #31 of 319 by trust here, #46 by citations
• Computer Vision and Pattern Recognition — 69 citations into it
• Optimization & Training — 53 citations into it
• Safety, Robustness & Interpretability — 43 citations into it
• Computational Theory and Mathematics — 36 citations into it
• Information Systems — 29 citations into it

Whom Tianjian Chen trusts overall

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

1. Cynthia Dwork
2. H. Brendan McMahan
3. Pascal Paillier
4. Yang Liu
5. Payman Mohassel
6. Alex Krizhevsky
7. Geoffrey E. Hinton
8. Kaiming He
9. Yoshua Bengio
10. Qiang Yang

Who trusts Tianjian Chen overall

1. Yang Liu
2. Qinbin Li
3. Xuefei Yin
4. Li Li
5. Zeyi Wen
6. Yan Kang
7. Omar Abdel Wahab
8. Jiahuan Luo
9. Chengliang Zhang
10. Shreya Sharma

Papers from Tianjian Chen's perspective

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

Their papers

• Federated Machine Learning (2019) — 3,214 citations
• Backdoor attacks and defenses in feature-partitioned collaborative learning (2020) — 64 citations
• Federated Learning (2019) — 1,040 citations
• FedBCD: A Communication-Efficient Collaborative Learning Framework for Distributed Features (2022) — 139 citations
• Self-supervised Cross-silo Federated Neural Architecture Search (2021) — 21 citations
• FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection (2021) — 312 citations
• FedVision: An Online Visual Object Detection Platform Powered by Federated Learning (2020) — 352 citations
• A Sustainable Incentive Scheme for Federated Learning (2020) — 119 citations
• Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention (2020) — 39 citations
• A Secure Federated Transfer Learning Framework (2020) — 549 citations
• A Communication Efficient Vertical Federated Learning Framework (2019) — 79 citations
• A Communication Efficient Collaborative Learning Framework for Distributed Features (2019) — 62 citations
• A Quasi-Newton Method Based Vertical Federated Learning Framework for Logistic Regression (2019) — 101 citations
• Federated Transfer Reinforcement Learning for Autonomous Driving (2019) — 131 citations
• SecureBoost: A Lossless Federated Learning Framework (2019) — 701 citations
• Secure Federated Transfer Learning (2018) — 128 citations