Thilo Strauss

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 88 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 43 citations into it; #19,496 of 21,575 by trust here, #10,677 by citations
• Artificial Intelligence — 27 citations into it; #20,716 of 35,345 by trust here, #23,731 by citations
• Generative Models — 19 citations into it; #1,788 of 1,956 by trust here, #1,200 by citations
• Safety, Robustness & Interpretability — 16 citations into it

Whom Thilo Strauss trusts overall

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

1. Alex Krizhevsky
2. Ian J. Goodfellow
3. Nicolas Papernot
4. Geoffrey E. Hinton
5. Christian Szegedy
6. Diederik P. Kingma
7. Yehuda Koren
8. Yann LeCun
9. Ilya Sutskever
10. Yoshua Bengio

Who trusts Thilo Strauss overall

1. David Stutz
2. Daniel Jakubovitz
3. Matthias Hein
4. Cihang Xie
5. Bernt Schiele
6. Raja Giryes
7. Sven Gowal
8. Sylvestre-Alvise Rebuffi
9. Mingxing Tan

Papers from Thilo Strauss's perspective

Ordered by how strongly the researchers Thilo Strauss 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. ImageNet: A large-scale hierarchical image database
8. Attention is All you Need (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Texture Fields: Learning Texture Representations in Function Space (2019) — 336 citations
• Ensemble Methods as a Defense to Adversarial Perturbations Against Deep Neural Networks (2017) — 149 citations