G. Suh

Artificial Intelligence · 2002–2019

A researcher in the Science corpus: 12 papers; trusts 142 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 88 citations into it; #11,274 of 35,345 by trust here, #8,706 by citations
• Computer Vision and Pattern Recognition — 67 citations into it; #15,445 of 21,575 by trust here, #13,269 by citations
• Efficiency & Compression — 38 citations into it
• Signal Processing — 28 citations into it; #3,004 of 7,572 by trust here, #2,184 by citations
• Computer Networks and Communications — 27 citations into it; #3,797 of 9,886 by trust here, #2,613 by citations
• Hardware and Architecture — 26 citations into it; #57 of 1,551 by trust here, #161 by citations
• Electrical and Electronic Engineering — 25 citations into it; #1,136 of 16,539 by trust here, #5,257 by citations
• Model Architectures — 19 citations into it

Whom G. Suh trusts overall

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

1. Blaise Gassend
2. P. Ravikanth
3. Rafail M. Ostrovsky
4. Whitfield Diffie
5. Srini Devadas
6. Dwaine E. Clarke
7. Alex Krizhevsky
8. Ravikanth Pappu
9. R. Rivest
10. Eric Rescorla

Who trusts G. Suh overall

1. Srini Devadas
2. Roel Maes
3. Victor Costan
4. Abhranil Maiti
5. Charles Herder
6. U. Rührmair
7. Ujjwal Guin
8. Ben Fisch
9. Jie Li
10. Charles W. O'Donnell

Papers from G. Suh's perspective

Ordered by how strongly the researchers G. Suh 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. ImageNet: A large-scale hierarchical image database
4. Learning Multiple Layers of Features from Tiny Images
5. Gradient-based learning applied to document recognition
6. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
7. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
8. Going deeper with convolutions
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Channel Gating Neural Networks (2018) — 207 citations
• Boosting the Performance of CNN Accelerators with Dynamic Fine-Grained Channel Gating (2019) — 48 citations
• Physical Unclonable Functions for Device Authentication and Secret Key Generation (2007) — 2,563 citations
• Extracting secret keys from integrated circuits (2005) — 1,048 citations
• A Hardware Design Language for Timing-Sensitive Information-Flow Security (2015) — 256 citations
• Efficient memory integrity verification and encryption for secure processors (2003) — 305 citations
• Reverse Engineering Convolutional Neural Networks Through Side-channel Information Leaks (2018) — 298 citations
• FPGA-Based Remote Power Side-Channel Attacks (2018) — 313 citations
• Aegis: A Single-Chip Secure Processor (2007) — 297 citations
• Caches and hash trees for efficient memory integrity verification (2003) — 296 citations
• AEGIS: architecture for tamper-evident and tamper-resistant processing (2003) — 619 citations
• A new memory monitoring scheme for memory-aware scheduling and partitioning (2002) — 323 citations