Marco Melis

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 37 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 110 citations into it
• Safety, Robustness & Interpretability — 109 citations into it
• Signal Processing — 67 citations into it
• Computer Networks and Communications — 46 citations into it
• Information Systems — 22 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Control and Systems Engineering — 8 citations into it
• Statistical Learning Theory — 8 citations into it

Whom Marco Melis trusts overall

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

1. Battista Biggio
2. Ambra Demontis
3. Fabio Roli
4. Alex Krizhevsky
5. Geoffrey E. Hinton
6. Christian Szegedy
7. Blaine Nelson
8. Paolo Russu
9. Ian J. Goodfellow
10. Ilya Sutskever

Who trusts Marco Melis overall

1. Davide Maiorca
2. Battista Biggio
3. Giorgio Giacinto
4. Naveed Akhtar
5. Ambra Demontis
6. Fabio Pierazzi
7. Matthew Wicker
8. Shilin Qiu
9. Zhibo Zhang
10. Feargus Pendlebury

Papers from Marco Melis's perspective

Ordered by how strongly the researchers Marco Melis 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Is Deep Learning Safe for Robot Vision? Adversarial Examples Against the iCub Humanoid (2017) — 102 citations
• secml: A Python Library for Secure and Explainable Machine Learning (2019) — 42 citations
• Explaining Black-box Android Malware Detection (2018) — 49 citations
• Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks (2018) — 477 citations
• Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection (2017) — 307 citations