Sruti Bhagavatula

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 3 papers; trusts 164 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #9,799 of 35,345 by trust here, #5,970 by citations
• Safety, Robustness & Interpretability — 62 citations into it; #666 of 2,251 by trust here, #468 by citations
• Computer Vision and Pattern Recognition — 48 citations into it; #7,467 of 21,575 by trust here, #4,402 by citations
• Signal Processing — 24 citations into it; #2,012 of 7,572 by trust here, #603 by citations
• Hardware and Architecture — 6 citations into it
• Optimization & Training — 6 citations into it
• Molecular Biology — 5 citations into it

Whom Sruti Bhagavatula trusts overall

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

1. Geoffrey E. Hinton
2. Andrea Vedaldi
3. Ian J. Goodfellow
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Yoshua Bengio
7. Christian Szegedy
8. Nicholas Carlini
9. L. Eon Bottou
10. Yann LeCun

Who trusts Sruti Bhagavatula overall

1. Logan Engstrom
2. Florian Tramèr
3. Edward Chou
4. Dimitris Tsipras
5. Ivan Evtimov
6. Danilo Vasconcellos Vargas
7. Nicholas Carlini
8. Nicolas Papernot
9. Hossein Hosseini
10. Eric Wong

Papers from Sruti Bhagavatula's perspective

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

Their papers

• Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition (2016) — 1,803 citations
• Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition (2018) — 99 citations
• Privacy Expectations and Preferences in an IoT World (2017) — 340 citations