Ashudeep Singh

Artificial Intelligence · 2005–2018

A researcher in the Science corpus: 3 papers; trusts 26 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 28 citations into it
• Information Systems — 21 citations into it; #5,890 of 8,771 by trust here, #3,540 by citations
• Management Science and Operations Research — 17 citations into it; #3,920 of 11,946 by trust here, #2,185 by citations
• Marketing — 8 citations into it
• Safety Research — 8 citations into it
• AI Ethics, Fairness & Society — 6 citations into it; #342 of 494 by trust here, #234 by citations
• Optimization & Training — 6 citations into it
• Safety, Robustness & Interpretability — 6 citations into it

Whom Ashudeep Singh trusts overall

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

1. Kalervo Järvelin
2. F. Dufossé
3. Cynthia Dwork
4. Matthew Richardson
5. Asia J. Biega
6. Thorsten Joachims
7. Moritz Hardt
8. Meike Zehlike
9. Yoshua Bengio
10. Richard S. Zemel

Who trusts Ashudeep Singh overall

1. Asia J. Biega
2. Harikrishna Narasimhan
3. Yifan Wang
4. Chen Gao
5. Sahin Cem Geyik
6. Krishna P. Gummadi
7. Andrew Cotter
8. Jinsung Yoon
9. Weizhi Ma
10. Jiawei Chen

Papers from Ashudeep Singh's perspective

Ordered by how strongly the researchers Ashudeep Singh 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
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

• Recommendations as Treatments: Debiasing Learning and Evaluation (2016) — 833 citations
• Fairness of Exposure in Rankings (2018) — 692 citations
• Elaboration likelihood model: A missing intrinsic emotional implication (2005) — 120 citations