Vincent Tjeng

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 3 papers; trusts 65 researchers and is trusted by 52 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it; #5,995 of 35,345 by trust here, #12,387 by citations
• Safety, Robustness & Interpretability — 69 citations into it; #418 of 2,251 by trust here, #930 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Electrical and Electronic Engineering — 14 citations into it
• Hardware and Architecture — 14 citations into it
• Program Analysis & Verification — 10 citations into it; #26 of 301 by trust here, #43 by citations
• Optimization & Training — 9 citations into it
• Computational Theory and Mathematics — 7 citations into it

Whom Vincent Tjeng trusts overall

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

1. Nicholas Carlini
2. Geoffrey E. Hinton
3. Christian Szegedy
4. Kaiming He
5. Alex Krizhevsky
6. Rüdiger Ehlers
7. Diederik P. Kingma
8. Guy Katz
9. Sergey Ioffe
10. Ian J. Goodfellow

Who trusts Vincent Tjeng overall

1. Eric Wong
2. Francesco Croce
3. Sven Gowal
4. Gagandeep Singh
5. Kai Y. Xiao
6. Hadi Salman
7. J. Kolter
8. Matthias Hein
9. Matt Jordan
10. Shiqi Wang

Papers from Vincent Tjeng's perspective

Ordered by how strongly the researchers Vincent Tjeng 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

• Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability (2018) — 211 citations
• Verifying Neural Networks with Mixed Integer Programming (2017) — 120 citations
• Evaluating Robustness of Neural Networks with Mixed Integer Programming (2017) — 967 citations