Stephen Bonner

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 2 papers; trusts 27 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it; #26,483 of 35,345 by trust here, #25,809 by citations
• Information Systems — 11 citations into it; #6,089 of 8,771 by trust here, #7,386 by citations
• Computer Vision and Pattern Recognition — 10 citations into it
• Management Science and Operations Research — 9 citations into it; #8,720 of 11,946 by trust here, #7,262 by citations
• Reinforcement Learning — 8 citations into it
• Recommender Systems — 7 citations into it
• Model Architectures — 6 citations into it

Whom Stephen Bonner trusts overall

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

1. Adith Swaminathan
2. Thorsten Joachims
3. Paul R. Rosenbaum
4. Ilya Sutskever
5. Donald B. Rubin
6. Yifan Hu
7. Geoffrey E. Hinton
8. F. Maxwell Harper
9. Paul Covington
10. Nicolas Courty

Who trusts Stephen Bonner overall

1. Jiawei Chen
2. Yu Zheng
3. Liuyi Yao
4. Yang Zhang
5. Fuli Feng
6. Xiangnan He
7. Chen Gao
8. Hande Dong
9. Zhixuan Chu

Papers from Stephen Bonner's perspective

Ordered by how strongly the researchers Stephen Bonner 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Predicting the Computational Cost of Deep Learning Models (2018) — 287 citations
• Causal embeddings for recommendation (2017) — 317 citations