Anirban Sarkar

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 206 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 41 citations into it; #22,745 of 35,345 by trust here, #10,207 by citations
• Computer Vision and Pattern Recognition — 40 citations into it; #14,045 of 21,575 by trust here, #8,296 by citations
• Safety, Robustness & Interpretability — 19 citations into it; #1,484 of 2,251 by trust here, #1,047 by citations
• Natural Language Processing — 8 citations into it
• Model Architectures — 7 citations into it
• Biophysics — 6 citations into it
• Causal Inference & Discovery — 5 citations into it

Whom Anirban Sarkar trusts overall

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

1. Geoffrey E. Hinton
2. Karen Simonyan
3. Alex Krizhevsky
4. Matthew D. Zeiler
5. Diederik P. Kingma
6. Yoshua Bengio
7. Kaiming He
8. Oriol Vinyals
9. Yann LeCun
10. Rob Fergus

Who trusts Anirban Sarkar overall

1. Gobinda Saha
2. Kaede Shiohara
3. Zizhao Zhang
4. Edward Chou
5. Tawsifur Rahman
6. Christian Rupprecht
7. Vanessa Buhrmester
8. Pantelis Linardatos
9. Yitao Peng
10. Xu Cheng

Papers from Anirban Sarkar's perspective

Ordered by how strongly the researchers Anirban Sarkar 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
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

• Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks (2017) — 3,283 citations
• Neural Network Attributions: A Causal Perspective (2019) — 168 citations