Philipp Benz

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 90 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 54 citations into it
• Computer Vision and Pattern Recognition — 49 citations into it
• Safety, Robustness & Interpretability — 29 citations into it
• Model Architectures — 14 citations into it

Whom Philipp Benz trusts overall

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

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

Papers from Philipp Benz's perspective

Ordered by how strongly the researchers Philipp Benz 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. ImageNet: A large-scale hierarchical image database
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• A Survey On Universal Adversarial Attack (2021) — 115 citations
• ResNet or DenseNet? Introducing Dense Shortcuts to ResNet (2020) — 153 citations