Claudia Eckert

Artificial Intelligence · 2012–2018

A researcher in the Science corpus: 5 papers; trusts 32 researchers and is trusted by 28 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #7,394 of 35,345 by trust here, #5,720 by citations
• Safety, Robustness & Interpretability — 58 citations into it; #515 of 2,251 by trust here, #547 by citations
• Signal Processing — 44 citations into it; #4,474 of 7,572 by trust here, #1,709 by citations
• Computer Networks and Communications — 42 citations into it; #5,672 of 9,886 by trust here, #1,422 by citations
• Information Systems — 30 citations into it
• Privacy & Security — 28 citations into it
• Computer Vision and Pattern Recognition — 11 citations into it
• Optimization & Training — 8 citations into it

Whom Claudia Eckert trusts overall

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

1. Battista Biggio
2. Blaine Nelson
3. Pavel Laskov
4. Yoshua Bengio
5. Amir Globerson
6. Marco Barreno
7. Geoffrey E. Hinton
8. Marius Kloft
9. Daniel Lowd
10. Robert Tibshirani

Who trusts Claudia Eckert overall

1. Battista Biggio
2. Katja Auernhammer
3. Jacob Steinhardt
4. Vale Tolpegin
5. Virat Shejwalkar
6. Ricky Laishram
7. Nicolas Papernot

Papers from Claudia Eckert's perspective

Ordered by how strongly the researchers Claudia Eckert 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. Dropout: a simple way to prevent neural networks from overfitting
7. Long Short-Term Memory
8. Attention is All you Need (Neural Information Processing Systems)
9. Et al
10. ImageNet: A large-scale hierarchical image database

Their papers

• Is Feature Selection Secure against Training Data Poisoning? (2015) — 445 citations
• Support vector machines under adversarial label contamination (2015) — 241 citations
• Adversarial Label Flips Attack on Support Vector Machines (2012) — 268 citations
• Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables (2018) — 354 citations
• Deep Learning for Classification of Malware System Call Sequences (2016) — 528 citations