P. Shanti Sastry

Artificial Intelligence · 2003–2020

A researcher in the Science corpus: 5 papers; trusts 181 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it; #11,709 of 35,345 by trust here, #14,438 by citations
• Computer Vision and Pattern Recognition — 35 citations into it
• Optimization & Training — 19 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Model Architectures — 12 citations into it
• Safety, Robustness & Interpretability — 9 citations into it
• Computational Theory and Mathematics — 6 citations into it; #4,355 of 12,182 by trust here, #2,629 by citations
• Industrial and Manufacturing Engineering — 6 citations into it

Whom P. Shanti Sastry trusts overall

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

1. Kaiming He
2. Richard S. Sutton
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Rakesh Agrawal
6. Lawrence R. Rabiner
7. Neil D. Lawrence
8. David Ruppert
9. Jakramate Bootkrajang
10. Yoshua Bengio

Who trusts P. Shanti Sastry overall

1. Chris HomesParker
2. Ole-Christoffer Granmo
3. Chao Lan
4. Chris HolmesParker
5. Xingjun Ma
6. Eduardo Fonseca
7. Yi-Wei Chen
8. Yisen Wang

Papers from P. Shanti Sastry's perspective

Ordered by how strongly the researchers P. Shanti Sastry 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Robust Loss Functions under Label Noise for Deep Neural Networks (2017) — 1,168 citations
• A survey of temporal data mining (2006) — 375 citations
• On Robustness of Neural Architecture Search Under Label Noise (2020) — 11 citations
• Robust Loss Functions for Learning Multi-class Classifiers (2018) — 17 citations
• Networks of Learning Automata: Techniques for Online Stochastic Optimization (2003) — 364 citations