Aniruddh Raghu

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 6 papers; trusts 94 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #17,819 of 35,345 by trust here, #8,810 by citations
• Reinforcement Learning — 43 citations into it; #1,130 of 2,242 by trust here, #1,118 by citations
• Epidemiology — 23 citations into it; #1,518 of 2,148 by trust here, #1,250 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #14,986 of 21,575 by trust here, #9,305 by citations
• Representation & Self-Supervised Learning — 18 citations into it; #2,362 of 3,145 by trust here, #1,722 by citations
• Statistics and Probability — 16 citations into it; #12,090 of 12,932 by trust here, #6,310 by citations
• Management Science and Operations Research — 13 citations into it
• Evaluation & Benchmarks — 12 citations into it

Whom Aniruddh Raghu trusts overall

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

1. Nan Jiang
2. Yoshua Bengio
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Chelsea Finn
6. Richard S. Sutton
7. Ronald J. Williams
8. Sergey Ioffe
9. Alex Krizhevsky
10. Christian Szegedy

Who trusts Aniruddh Raghu overall

1. Chao Yu
2. Chaochao Lu
3. Zhenyi Wang
4. Jiming Liu
5. Maithra Raghu
6. Zachary B. Charles
7. Steinar Laenen
8. Bernhard Schölkopf
9. Matthieu Komorowski
10. Kaiyi Ji

Papers from Aniruddh Raghu's perspective

Ordered by how strongly the researchers Aniruddh Raghu 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML (2019) — 756 citations
• Representation Balancing MDPs for Off-Policy Policy Evaluation (2018) — 75 citations
• Continuous State-Space Models for Optimal Sepsis Treatment: a Deep Reinforcement Learning Approach (2017) — 238 citations
• Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters (2018) — 37 citations
• Model-Based Reinforcement Learning for Sepsis Treatment (2018) — 60 citations
• Deep Reinforcement Learning for Sepsis Treatment (2017) — 200 citations