Shayak Sen

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 5 papers; trusts 91 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #6,642 of 35,345 by trust here, #6,517 by citations
• Safety, Robustness & Interpretability — 32 citations into it; #492 of 2,251 by trust here, #723 by citations
• Computer Vision and Pattern Recognition — 28 citations into it
• AI Ethics, Fairness & Society — 13 citations into it; #487 of 494 by trust here, #396 by citations
• Safety Research — 13 citations into it; #552 of 5,829 by trust here, #664 by citations
• Statistics and Probability — 10 citations into it
• Information Systems — 9 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it

Whom Shayak Sen trusts overall

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

1. Lloyd S. Shapley
2. Robert Tibshirani
3. Karen Simonyan
4. Alex Krizhevsky
5. Cynthia Dwork
6. Judea Pearl
7. Geoffrey E. Hinton
8. Stefan Rüping
9. Yann LeCun
10. Yoshua Bengio

Who trusts Shayak Sen overall

1. Scott M. Lundberg
2. Mukund Sundararajan
3. Ian Covert
4. Su-In Lee
5. Klas Leino
6. Giulia Vilone
7. Gabriel Erion-Barner
8. Luke Merrick
9. Nadia Burkart
10. Simon Caton

Papers from Shayak Sen's perspective

Ordered by how strongly the researchers Shayak Sen 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. Learning Multiple Layers of Features from Tiny Images
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Going deeper with convolutions
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. Long Short-Term Memory
9. ImageNet: A large-scale hierarchical image database
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Feature-Wise Bias Amplification (2018) — 52 citations
• Algorithmic Transparency via Quantitative Input Influence: Theory and Experiments with Learning Systems (2016) — 783 citations
• Influence-Directed Explanations for Deep Convolutional Networks (2018) — 80 citations
• Proxy Non-Discrimination in Data-Driven Systems (2017) — 59 citations
• Debugging Machine Learning Tasks (2016) — 47 citations