Michael C. Hughes

Artificial Intelligence · with Doshi-Velez · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 77 researchers and is trusted by 40 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #5,462 of 35,345 by trust here, #6,378 by citations
• Safety, Robustness & Interpretability — 25 citations into it; #396 of 2,251 by trust here, #542 by citations
• Computer Vision and Pattern Recognition — 18 citations into it
• Natural Language Processing — 14 citations into it
• Reinforcement Learning — 13 citations into it
• Epidemiology — 11 citations into it
• Medical & Health AI — 11 citations into it
• Model Architectures — 6 citations into it; #519 of 2,278 by trust here, #894 by citations

Whom Michael C. Hughes trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. David M. Blei
4. Yoshua Bengio
5. Alex Krizhevsky
6. Yann LeCun
7. Ilya Sutskever
8. Jimmy Ba
9. Radford M. Neal
10. Sepp Hochreiter

Who trusts Michael C. Hughes overall

1. Gagan Bansal
2. Menaka Narayanan
3. Sina Mohseni
4. Andrew Slavin Ross
5. Isaac Lage
6. Leilani H. Gilpin
7. Been Kim
8. Stefano Teso
9. Wojciech Samek
10. Emily Chen

Papers from Michael C. Hughes's perspective

Ordered by how strongly the researchers Michael C. Hughes 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations (2017) — 691 citations
• POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning (2020) — 62 citations
• Beyond Sparsity: Tree Regularization of Deep Models for Interpretability (2017) — 300 citations
• Supervised topic models for clinical interpretability (2016) — 9 citations