Luca Daniel

Safety, Robustness & Interpretability · 2018

A researcher in the Science corpus: 3 papers; trusts 37 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Safety, Robustness & Interpretability — 79 citations into it; #1,277 of 2,251 by trust here, #743 by citations
• Artificial Intelligence — 78 citations into it; #10,040 of 35,345 by trust here, #7,010 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #18,338 of 21,575 by trust here, #12,689 by citations
• Signal Processing — 18 citations into it
• Hardware and Architecture — 13 citations into it
• Electrical and Electronic Engineering — 6 citations into it; #4,137 of 16,539 by trust here, #5,200 by citations
• Program Analysis & Verification — 6 citations into it; #295 of 301 by trust here, #60 by citations

Whom Luca Daniel trusts overall

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

1. Nicholas Carlini
2. Ian J. Goodfellow
3. Christian Szegedy
4. Geoffrey E. Hinton
5. Nicolas Papernot
6. Alex Krizhevsky
7. Kaiming He
8. Yoshua Bengio
9. Alexey Kurakin
10. Yann LeCun

Who trusts Luca Daniel overall

1. Huan Zhang
2. Matt Jordan
3. Shiqi Wang

Papers from Luca Daniel's perspective

Ordered by how strongly the researchers Luca Daniel 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. Gradient-based learning applied to document recognition
4. Learning Multiple Layers of Features from Tiny Images
5. Intriguing properties of neural networks
6. ImageNet: A large-scale hierarchical image database
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. Distilling the Knowledge in a Neural Network (arXiv.org)

Their papers

• Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach (2018) — 524 citations
• Towards Fast Computation of Certified Robustness for ReLU Networks (2018) — 755 citations
• Efficient Neural Network Robustness Certification with General Activation Functions (2018) — 936 citations