Sayash Kapoor

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it
• Evaluation & Benchmarks — 14 citations into it
• Statistics and Probability — 11 citations into it
• Sociology and Political Science — 10 citations into it

Whom Sayash Kapoor trusts overall

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

1. Leo Breiman
2. Michael D. Ward
3. Jake M. Hofman
4. Robert Geirhos
5. Xavier Robin
6. Peter J. Rousseeuw
7. Julia Dressel
8. Momin M. Malik
9. Brian Greenhill
10. Duncan J. Watts

Papers from Sayash Kapoor's perspective

Ordered by how strongly the researchers Sayash Kapoor 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet Large Scale Visual Recognition Challenge
8. Long Short-Term Memory
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• Controlling Polarization in Personalization: An Algorithmic Framework (2019) — 99 citations
• Leakage and the reproducibility crisis in machine-learning-based science (2023) — 733 citations