Z. Berkay Celik

Artificial Intelligence · 2015–2016

A researcher in the Science corpus: 4 papers; trusts 35 researchers and is trusted by 167 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #1,871 of 35,345 by trust here, #1,206 by citations
• Safety, Robustness & Interpretability — 55 citations into it; #84 of 2,251 by trust here, #43 by citations
• Signal Processing — 35 citations into it; #1,130 of 7,572 by trust here, #191 by citations
• Computer Vision and Pattern Recognition — 26 citations into it
• Computer Networks and Communications — 21 citations into it
• Privacy & Security — 14 citations into it
• Information Systems — 12 citations into it
• Evaluation & Benchmarks — 10 citations into it

Whom Z. Berkay Celik trusts overall

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

1. Yann LeCun
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Christian Szegedy
5. Alex Krizhevsky
6. Ian J. Goodfellow
7. Ilya Sutskever
8. Battista Biggio
9. Diederik P. Kingma
10. Ruslan Salakhutdinov

Who trusts Z. Berkay Celik overall

1. Nicolas Papernot
2. Nicholas Carlini
3. Patrick Drew McDaniel
4. Ian J. Goodfellow
5. Nina Narodytska
6. Florian Tramèr
7. Jamie Hayes
8. Jonas Rauber
9. Pin-Yu Chen
10. Wieland Brendel

Papers from Z. Berkay Celik's perspective

Ordered by how strongly the researchers Z. Berkay Celik 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. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. Going deeper with convolutions
8. ImageNet: A large-scale hierarchical image database
9. GENERATIVE ADVERSARIAL NETS
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Practical Black-Box Attacks against Machine Learning (2016) — 4,113 citations
• The Limitations of Deep Learning in Adversarial Settings (2015) — 4,317 citations
• Machine Learning in Adversarial Settings (2016) — 143 citations
• Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples (2016) — 496 citations