Kyle D. Julian

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 21 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 57 citations into it; #4,596 of 35,345 by trust here, #4,420 by citations
• Safety, Robustness & Interpretability — 27 citations into it; #361 of 2,251 by trust here, #399 by citations
• Computer Vision and Pattern Recognition — 20 citations into it; #5,081 of 21,575 by trust here, #4,152 by citations
• Program Analysis & Verification — 15 citations into it
• Computational Theory and Mathematics — 13 citations into it; #1,587 of 12,182 by trust here, #567 by citations
• Model Architectures — 9 citations into it
• Software — 7 citations into it
• Aerospace Engineering — 6 citations into it; #3,288 of 6,421 by trust here, #4,066 by citations

Whom Kyle D. Julian trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Matthew D. Zeiler
4. Diederik P. Kingma
5. Mykel J. Kochenderfer
6. Ilya Sutskever
7. R´emi Munos
8. Yann LeCun
9. John C. Duchi
10. Xiaowei Huang

Who trusts Kyle D. Julian overall

1. Guy Katz
2. Clark W. Barrett
3. Nicholas Carlini
4. Shiqi Wang
5. Divya Gopinath
6. Kexin Pei
7. Aditi Raghunathan
8. David L. Dill
9. Timon Gehr
10. Hung Viet Pham

Papers from Kyle D. Julian's perspective

Ordered by how strongly the researchers Kyle D. Julian 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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks (2017) — 2,155 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
• Policy compression for aircraft collision avoidance systems (2016) — 272 citations
• Toward Scalable Verification for Safety-Critical Deep Networks (2018) — 40 citations