Guy Katz

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 8 papers; trusts 53 researchers and is trusted by 63 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it; #3,394 of 35,345 by trust here, #3,895 by citations
• Safety, Robustness & Interpretability — 76 citations into it; #234 of 2,251 by trust here, #324 by citations
• Computer Vision and Pattern Recognition — 34 citations into it; #5,242 of 21,575 by trust here, #4,152 by citations
• Program Analysis & Verification — 25 citations into it
• Computational Theory and Mathematics — 20 citations into it; #1,672 of 12,182 by trust here, #553 by citations
• Hardware and Architecture — 15 citations into it
• Signal Processing — 15 citations into it
• Automotive Engineering — 13 citations into it

Whom Guy Katz trusts overall

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

1. Xiaowei Huang
2. Geoffrey E. Hinton
3. Luca Pulina
4. Ian J. Goodfellow
5. Christian Szegedy
6. Alex Krizhevsky
7. Clark W. Barrett
8. Nicholas Carlini
9. Kyle D. Julian
10. Ilya Sutskever

Who trusts Guy Katz overall

1. Aditi Raghunathan
2. Nicholas Carlini
3. Timon Gehr
4. J. Kolter
5. Shiqi Wang
6. Huan Zhang
7. Eric Wong
8. Vincent Tjeng
9. Xiaowei Huang
10. Wenjie Ruan

Papers from Guy Katz's perspective

Ordered by how strongly the researchers Guy Katz 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. 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks (2017) — 2,155 citations
• Ground-Truth Adversarial Examples (2017) — 91 citations
• Towards Proving the Adversarial Robustness of Deep Neural Networks (2017) — 128 citations
• The Marabou Framework for Verification and Analysis of Deep Neural Networks (2019) — 622 citations
• DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks (2017) — 93 citations
• Lazy proofs for DPLL(T)-based SMT solvers (2016) — 35 citations
• Toward Scalable Verification for Safety-Critical Deep Networks (2018) — 40 citations
• Provably Minimally-Distorted Adversarial Examples (2017) — 132 citations