Amartya Sanyal

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 2 papers; trusts 38 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #34,839 of 35,345 by trust here, #25,809 by citations
• Computer Vision and Pattern Recognition — 19 citations into it; #21,261 of 21,575 by trust here, #16,787 by citations
• Privacy & Security — 11 citations into it
• Efficiency & Compression — 9 citations into it
• Computational Theory and Mathematics — 7 citations into it
• Natural Language Processing — 5 citations into it

Whom Amartya Sanyal trusts overall

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

1. Craig Gentry
2. Matthieu Courbariaux
3. Alex Krizhevsky
4. Yoshua Bengio
5. Kaiming He
6. Geoffrey E. Hinton
7. Yann LeCun
8. M. Sadegh Riazi
9. Minje Kim
10. Richard Socher

Who trusts Amartya Sanyal overall

1. Ali Shahin Shamsabadi
2. Alon Brutzkus
3. Assi Barak
4. Nitin Agrawal
5. Ho Bae
6. Brandon Reagen
7. Seyed Ali Osia
8. Oren Elisha
9. Daniel E. Escudero

Papers from Amartya Sanyal's perspective

Ordered by how strongly the researchers Amartya Sanyal trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. ImageNet: A large-scale hierarchical image database
5. Learning Multiple Layers of Features from Tiny Images
6. Attention is All you Need (Neural Information Processing Systems)
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Long Short-Term Memory
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

• Calibrating Deep Neural Networks using Focal Loss (2020) — 646 citations
• TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service (2018) — 135 citations