Manuel Günther

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 7 papers; trusts 696 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 130 citations into it; #10,350 of 35,345 by trust here, #8,619 by citations
• Computer Vision and Pattern Recognition — 124 citations into it; #12,349 of 21,575 by trust here, #8,502 by citations
• Safety, Robustness & Interpretability — 42 citations into it
• Signal Processing — 35 citations into it
• Computer Networks and Communications — 25 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Model Architectures — 11 citations into it

Whom Manuel Günther trusts overall

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

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

Who trusts Manuel Günther overall

1. Naveed Akhtar
2. Samuel F. Dodge
3. Jason Jo
4. Daniel Jakubovitz
5. Vikash Sehwag
6. Chuanxing Geng
7. Zhen Fang
8. Martin Mundt
9. Jingkang Yang
10. Yen-Chang Hsu

Papers from Manuel Günther's perspective

Ordered by how strongly the researchers Manuel Günther 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

• The Overlooked Elephant of Object Detection: Open Set (2020) — 199 citations
• Reducing Network Agnostophobia (2018) — 383 citations
• Are Accuracy and Robustness Correlated (2016) — 62 citations
• Towards Robust Deep Neural Networks with BANG (2016) — 78 citations
• LOTS about attacking deep features (2016) — 44 citations
• Learning and the Unknown: Surveying Steps toward Open World Recognition (2019) — 159 citations
• A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions (2016) — 166 citations