Sayak Paul

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 528 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 168 citations into it; #32,587 of 35,345 by trust here, #21,316 by citations
• Computer Vision and Pattern Recognition — 154 citations into it; #20,011 of 21,575 by trust here, #14,151 by citations
• Model Architectures — 44 citations into it
• Signal Processing — 28 citations into it
• Representation & Self-Supervised Learning — 26 citations into it
• Optimization & Training — 24 citations into it
• Safety, Robustness & Interpretability — 23 citations into it; #2,052 of 2,251 by trust here, #1,780 by citations
• Generative Models — 22 citations into it

Whom Sayak Paul trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Ilya Sutskever
7. Jimmy Ba
8. Yann LeCun
9. Ross B. Girshick
10. Christian Szegedy

Who trusts Sayak Paul overall

1. Francesco Pelosin
2. P. Xu
3. Yutong Bai
4. Stanislav Fort
5. Jie Jessie Ren
6. Namuk Park
7. Muzammal Naseer
8. Lukas Hoyer

Papers from Sayak Paul's perspective

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

Their papers

• Vision Transformers are Robust Learners (2021) — 386 citations
• A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019) — 402 citations