Reproducibility Summary

Artificial Intelligence · 2021

A researcher in the Science corpus: 2 papers; trusts 112 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it
• Safety, Robustness & Interpretability — 12 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it

Whom Reproducibility Summary trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Yann LeCun
4. J. Kolter
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Christian Szegedy
8. Nicholas Carlini
9. X. Zhang
10. Aleksander Madry

Who trusts Reproducibility Summary overall

1. Francesco Croce
2. Tianyu Pang
3. A. Serban
4. Yoshitomo Matsubara
5. Naveed Akhtar
6. Florian Tramèr
7. Wenguan Wang
8. Zekai Wang
9. Matthias Hein
10. Tianfei Zhou

Papers from Reproducibility Summary's perspective

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

Their papers

• Reproducibility Report: Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness (2021)
• Resolution Adaptive Networks for Efficient Inference (2021)