Roland S. Zimmermann

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 174 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 90 citations into it
• Computer Vision and Pattern Recognition — 71 citations into it
• Safety, Robustness & Interpretability — 43 citations into it
• Representation & Self-Supervised Learning — 30 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Media Technology — 6 citations into it
• Industrial and Manufacturing Engineering — 5 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Roland S. Zimmermann trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Aapo Hyvärinen
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Yoshua Bengio
7. Ross B. Girshick
8. Yann LeCun
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Roland S. Zimmermann overall

1. Sébastien Lachapelle
2. Julius von Kügelgen
3. Yash Sharma

Papers from Roland S. Zimmermann's perspective

Ordered by how strongly the researchers Roland S. Zimmermann trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• A Simple Way to Make Neural Networks Robust Against Diverse Image Corruptions (2020) — 243 citations
• Contrastive Learning Inverts the Data Generating Process (2021) — 283 citations
• Increasing the robustness of DNNs against image corruptions by playing the Game of Noise (2020) — 66 citations