Tribhuvanesh Orekondy

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 4 papers; trusts 596 researchers and is trusted by 21 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 166 citations into it; #7,575 of 35,345 by trust here, #15,866 by citations
• Computer Vision and Pattern Recognition — 95 citations into it
• Safety, Robustness & Interpretability — 68 citations into it; #525 of 2,251 by trust here, #1,371 by citations
• Privacy & Security — 36 citations into it
• Optimization & Training — 25 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Signal Processing — 22 citations into it
• Efficiency & Compression — 20 citations into it

Whom Tribhuvanesh Orekondy trusts overall

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

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

Who trusts Tribhuvanesh Orekondy overall

1. Adam Dziedzic
2. Eric Wallace
3. Pratyush Maini
4. Maria Rigaki
5. Sebastian Szyller
6. Nicolas Papernot
7. Muhammad Ahmad Kaleem
8. Matthew Jagielski
9. Hengrui Jia
10. Mitchell Stern

Papers from Tribhuvanesh Orekondy's perspective

Ordered by how strongly the researchers Tribhuvanesh Orekondy 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. Long Short-Term Memory
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Knockoff Nets: Stealing Functionality of Black-Box Models (2018) — 665 citations
• Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks (2019) — 200 citations
• Understanding and Controlling User Linkability in Decentralized Learning (2018) — 14 citations
• Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning (2018) — 43 citations