Kareem Amin

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 4 papers; trusts 30 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it; #27,548 of 35,345 by trust here, #25,809 by citations
• Privacy & Security — 23 citations into it
• Reinforcement Learning — 16 citations into it; #1,863 of 2,242 by trust here, #1,932 by citations
• Computational Theory and Mathematics — 11 citations into it
• Statistics and Probability — 9 citations into it

Whom Kareem Amin trusts overall

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

1. Cynthia Dwork
2. Jayadev Acharya
3. Liam Paninski
4. John C. Duchi
5. Adam D. Smith
6. Andrew Y. Ng
7. Ilias Diakonikolas
8. Peter Auer
9. Pieter Abbeel
10. Kobbi Nissim

Who trusts Kareem Amin overall

1. Justin Fu
2. Evan Hubinger
3. Om Thakkar
4. Victor Balcer
5. Xuefei Yin
6. Albert Cheu
7. Jayadev Acharya
8. Tom Everitt
9. Yuxi Li
10. Katie Luo

Papers from Kareem Amin's perspective

Ordered by how strongly the researchers Kareem Amin trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. 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

• Repeated Inverse Reinforcement Learning (2017) — 78 citations
• Towards Resolving Unidentifiability in Inverse Reinforcement Learning (2016) — 34 citations
• Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy (2019) — 78 citations
• Pan-Private Uniformity Testing (2019) — 25 citations