Adrian Vladu

Artificial Intelligence · 2015–2017

A researcher in the Science corpus: 3 papers; trusts 19 researchers and is trusted by 729 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #1,195 of 35,345 by trust here, #1,345 by citations
• Safety, Robustness & Interpretability — 32 citations into it; #71 of 2,251 by trust here, #81 by citations
• Computer Vision and Pattern Recognition — 13 citations into it; #1,632 of 21,575 by trust here, #1,038 by citations
• Computational Theory and Mathematics — 10 citations into it; #11,450 of 12,182 by trust here, #7,142 by citations
• Signal Processing — 8 citations into it
• Numerical Analysis — 7 citations into it; #3,391 of 3,586 by trust here, #2,107 by citations

Whom Adrian Vladu trusts overall

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

1. Aharon Ben-Tal
2. Daniel A. Spielman
3. Philip Wolfe
4. M. Sion
5. Kaiming He
6. Marco Cuturi
7. Leonid Gurvits
8. Alex Krizhevsky
9. Geoffrey E. Hinton
10. Bernhard Schölkopf

Who trusts Adrian Vladu overall

1. Nicholas Carlini
2. Matthias Hein
3. Francesco Croce
4. Maksym Andriushchenko
5. Florian Tramèr
6. Dan Hendrycks
7. Tianyu Pang
8. Eric Wong
9. Logan Engstrom
10. Cihang Xie

Papers from Adrian Vladu's perspective

Ordered by how strongly the researchers Adrian Vladu 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. Intriguing properties of neural networks
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Long Short-Term Memory
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Towards Deep Learning Models Resistant to Adversarial Attacks (2017) — 15,165 citations
• Matrix Scaling and Balancing via Box Constrained Newton's Method and Interior Point Methods (2017) — 123 citations
• Tight Bounds for Approximate Carathéodory and Beyond (2015) — 38 citations