Shike Mei

Artificial Intelligence · 2015

A researcher in the Science corpus: 3 papers; trusts 31 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 26 citations into it; #4,291 of 35,345 by trust here, #9,465 by citations
• Safety, Robustness & Interpretability — 25 citations into it
• Computer Networks and Communications — 23 citations into it
• Information Systems — 19 citations into it
• Signal Processing — 17 citations into it
• Privacy & Security — 16 citations into it; #228 of 709 by trust here, #503 by citations
• Natural Language Processing — 6 citations into it
• Mathematical Optimization — 5 citations into it

Whom Shike Mei trusts overall

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

1. Xiaojin Zhu
2. Pavel Laskov
3. Benoît Colson
4. C. Burges
5. Marco Barreno
6. Simon Pak Ho Chung
7. Blaine Nelson
8. T. Griffiths
9. Andreas Krause
10. Hui-Ching Lin

Who trusts Shike Mei overall

1. Pang Wei Koh
2. Xiaojin Zhu
3. Bo Li
4. Jacob Steinhardt
5. Percy Liang
6. Chang Liu
7. Battista Biggio
8. Mengchen Zhao
9. Matthew Jagielski
10. Luis Muñoz-González

Papers from Shike Mei's perspective

Ordered by how strongly the researchers Shike Mei 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. Gradient-based learning applied to document recognition
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners (2015) — 435 citations
• The Security of Latent Dirichlet Allocation (2015) — 72 citations
• Some Submodular Data-Poisoning Attacks on Machine Learners (2015)