Chris J. M. Yoon

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 106 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 157 citations into it; #12,279 of 35,345 by trust here, #11,624 by citations
• Safety, Robustness & Interpretability — 75 citations into it
• Privacy & Security — 57 citations into it; #327 of 709 by trust here, #306 by citations
• Federated & Distributed Learning — 42 citations into it; #117 of 319 by trust here, #138 by citations
• Computer Networks and Communications — 36 citations into it
• Information Systems — 33 citations into it
• Signal Processing — 29 citations into it
• Optimization & Training — 23 citations into it

Whom Chris J. M. Yoon trusts overall

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

1. L. Eon Bottou
2. Yann LeCun
3. Blaine Nelson
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Battista Biggio
7. Alex Krizhevsky
8. Marco Barreno
9. H. Brendan McMahan
10. John R. Douceur

Who trusts Chris J. M. Yoon overall

1. Jiawen Kang
2. Lingjuan Lyu
3. Zehui Xiong
4. Clement Fung
5. Dusist Niyato
6. Chien-Lun Chen
7. Minghong Fang
8. Lixu Wang
9. Han Yu
10. Mang Ye

Papers from Chris J. M. Yoon's perspective

Ordered by how strongly the researchers Chris J. M. Yoon 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Mitigating Sybils in Federated Learning Poisoning (2018) — 670 citations
• Biscotti: A Blockchain System for Private and Secure Federated Learning (2021) — 329 citations
• The Limitations of Federated Learning in Sybil Settings (2020) — 567 citations
• Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning (2018) — 88 citations