Andras Rozsa

Artificial Intelligence · 2016

A researcher in the Science corpus: 6 papers; trusts 521 researchers and is trusted by 23 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 95 citations into it; #7,451 of 35,345 by trust here, #9,092 by citations
• Computer Vision and Pattern Recognition — 84 citations into it; #7,329 of 21,575 by trust here, #8,952 by citations
• Safety, Robustness & Interpretability — 47 citations into it; #767 of 2,251 by trust here, #1,437 by citations
• Signal Processing — 36 citations into it
• Computer Networks and Communications — 24 citations into it
• Model Architectures — 19 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Hardware and Architecture — 8 citations into it; #1,092 of 1,551 by trust here, #1,291 by citations

Whom Andras Rozsa trusts overall

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

1. Christian Szegedy
2. Alex Krizhevsky
3. Ian J. Goodfellow
4. Yann LeCun
5. Geoffrey E. Hinton
6. Kaiming He
7. Ilya Sutskever
8. Karen Simonyan
9. Yoshua Bengio
10. Ross B. Girshick

Who trusts Andras Rozsa overall

1. Naveed Akhtar
2. Nicholas Carlini
3. Ajmal Saeed Mian
4. Manuel Günther
5. Terrance E. Boult
6. Daniel Jakubovitz
7. Samuel F. Dodge
8. Jason Jo
9. Mahmood Sharif
10. Seong Joon Oh

Papers from Andras Rozsa's perspective

Ordered by how strongly the researchers Andras Rozsa 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Are Accuracy and Robustness Correlated (2016) — 62 citations
• Adversarial Diversity and Hard Positive Generation (2016) — 268 citations
• Towards Robust Deep Neural Networks with BANG (2016) — 78 citations
• Assessing Threat of Adversarial Examples on Deep Neural Networks (2016) — 60 citations
• LOTS about attacking deep features (2016) — 44 citations
• A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions (2016) — 166 citations