Samantha Hansen

Artificial Intelligence · 2014–2018

A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 23 citations into it; #7,225 of 35,345 by trust here, #11,327 by citations
• Optimization & Training — 19 citations into it; #1,159 of 2,601 by trust here, #1,178 by citations
• Reinforcement Learning — 13 citations into it; #952 of 2,242 by trust here, #2,125 by citations
• Computer Vision and Pattern Recognition — 8 citations into it
• Computational Mechanics — 7 citations into it; #2,793 of 10,001 by trust here, #2,252 by citations

Whom Samantha Hansen trusts overall

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

1. Martin A. Riedmiller
2. Jorge Nocedal
3. Longxin Lin
4. G. Tesauro
5. Christopher J. C. H. Watkins
6. Geoffrey E. Hinton
7. Stephen J. Wright
8. Peter S. Dayan
9. Alex Krizhevsky
10. Richard S. Sutton

Who trusts Samantha Hansen overall

1. Maren Mahsereci
2. Jie Fu
3. Jakub Konecný
4. Philipp Hennig
5. Murat A. Erdogdu
6. Aryan Mokhtari
7. Zichuan Lin
8. Mark Eisen
9. Ke Li
10. Yushu Chen

Papers from Samantha Hansen's perspective

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

Their papers

• A Stochastic Quasi-Newton Method for Large-Scale Optimization (2014) — 525 citations
• Using Deep Q-Learning to Control Optimization Hyperparameters (2016) — 45 citations
• Explore, exploit, and explain: personalizing explainable recommendations with bandits (2018) — 185 citations