Beat Buesser

Safety, Robustness & Interpretability · 2018

A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Safety, Robustness & Interpretability — 43 citations into it

Whom Beat Buesser trusts overall

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

1. Ian J. Goodfellow

Papers from Beat Buesser's perspective

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

1. Intriguing properties of neural networks
2. ImageNet classification with deep convolutional neural networks
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Explaining and Harnessing Adversarial Examples
5. Gradient-based learning applied to document recognition
6. Learning Multiple Layers of Features from Tiny Images
7. Long Short-Term Memory
8. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
9. Attention is All you Need (Neural Information Processing Systems)
10. Towards Deep Learning Models Resistant to Adversarial Attacks

Their papers

• Adversarial Robustness Toolbox v1.0.0 (2018) — 573 citations