Sameer Wagh

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 56 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it; #22,720 of 35,345 by trust here, #18,186 by citations
• Privacy & Security — 51 citations into it
• Safety, Robustness & Interpretability — 25 citations into it
• Computer Vision and Pattern Recognition — 20 citations into it
• Information Systems — 19 citations into it
• Computational Theory and Mathematics — 17 citations into it
• Computer Networks and Communications — 13 citations into it
• Signal Processing — 11 citations into it

Whom Sameer Wagh trusts overall

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

1. Ran Canetti
2. Payman Mohassel
3. Yann LeCun
4. Donald Beaver
5. Jian Liu
6. Chiraag Shashikant Juvekar
7. Dan Bogdanov
8. Octavian Catrina
9. M. Sadegh Riazi
10. L. Eon Bottou

Who trusts Sameer Wagh overall

1. Assi Barak
2. Brian Knott
3. Rahul Rachuri
4. Hanieh Hashemi
5. Georgios Kaissis
6. Daniel E. Escudero
7. Nitin Agrawal
8. Shobha Venkataraman
9. Ajith Suresh
10. Anders Dalskov

Papers from Sameer Wagh's perspective

Ordered by how strongly the researchers Sameer Wagh 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Falcon: Honest-Majority Maliciously Secure Framework for Private Deep Learning (2020) — 389 citations
• Towards Probabilistic Verification of Machine Unlearning (2020) — 86 citations
• SecureNN: 3-Party Secure Computation for Neural Network Training (2019) — 486 citations
• SecureNN: Efficient and Private Neural Network Training (2018) — 125 citations