Luis Muñoz-González

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 5 papers; trusts 313 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 105 citations into it; #16,161 of 35,345 by trust here, #7,858 by citations
• Safety, Robustness & Interpretability — 83 citations into it; #2,112 of 2,251 by trust here, #903 by citations
• Computer Vision and Pattern Recognition — 44 citations into it
• Signal Processing — 44 citations into it
• Computer Networks and Communications — 39 citations into it
• Privacy & Security — 22 citations into it
• Information Systems — 18 citations into it
• Optimization & Training — 12 citations into it

Whom Luis Muñoz-González trusts overall

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

1. Alex Krizhevsky
2. Yoshua Bengio
3. Yann LeCun
4. Geoffrey E. Hinton
5. Karen Simonyan
6. Battista Biggio
7. Ian J. Goodfellow
8. Kaiming He
9. Christian Szegedy
10. Ross B. Girshick

Who trusts Luis Muñoz-González overall

1. Lie He
2. Battista Biggio
3. Virat Shejwalkar
4. Li Li
5. Sai Praneeth Karimireddy
6. Vale Tolpegin
7. Kang Liu
8. Minghong Fang
9. Matthew Jagielski
10. Amir Houmansadr

Papers from Luis Muñoz-González's perspective

Ordered by how strongly the researchers Luis Muñoz-González 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection (2018) — 166 citations
• Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization (2017) — 689 citations
• Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks (2018) — 78 citations
• Byzantine-Robust Federated Machine Learning through Adaptive Model Averaging (2019) — 229 citations
• Automated Dynamic Analysis of Ransomware: Benefits, Limitations and use for Detection (2016) — 299 citations