Alina Oprea

Artificial Intelligence · 2009–2020

A researcher in the Science corpus: 13 papers; trusts 68 researchers and is trusted by 54 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 228 citations into it; #4,707 of 35,345 by trust here, #2,292 by citations
• Safety, Robustness & Interpretability — 145 citations into it; #534 of 2,251 by trust here, #222 by citations
• Signal Processing — 72 citations into it; #6,689 of 7,572 by trust here, #5,086 by citations
• Computer Networks and Communications — 66 citations into it; #3,404 of 9,886 by trust here, #2,927 by citations
• Privacy & Security — 56 citations into it
• Information Systems — 47 citations into it; #1,436 of 8,771 by trust here, #1,246 by citations
• Computer Vision and Pattern Recognition — 29 citations into it
• Natural Language Processing — 26 citations into it

Whom Alina Oprea trusts overall

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

1. Battista Biggio
2. Shike Mei
3. Xiaojin Zhu
4. Alex Krizhevsky
5. Yudong Chen
6. Blaine Nelson
7. Geoffrey E. Hinton
8. Cynthia Dwork
9. Guofei Gu
10. Christian Szegedy

Who trusts Alina Oprea overall

1. Giuseppe Ateniese
2. Nicholas Carlini
3. Binghui Wang
4. Qian Wang
5. Vale Tolpegin
6. Katja Auernhammer
7. Zhiyi Tian
8. Florian Tramèr
9. Virat Shejwalkar
10. Matthew Jagielski

Papers from Alina Oprea's perspective

Ordered by how strongly the researchers Alina Oprea 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. Learning Multiple Layers of Features from Tiny Images
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Long Short-Term Memory
9. Intriguing properties of neural networks
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Extracting Training Data from Large Language Models (2020) — 3,168 citations
• Auditing Differentially Private Machine Learning: How Private is Private SGD? (2020) — 331 citations
• Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning (2018) — 882 citations
• Differentially Private Fair Learning (2018) — 174 citations
• Exploring Backdoor Poisoning Attacks Against Malware Classifiers (2020) — 18 citations
• Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks (2018) — 477 citations
• Robust Linear Regression Against Training Data Poisoning (2017) — 99 citations
• Robust High-Dimensional Linear Regression (2016)
• Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data (2014) — 186 citations
• Beehive: large-scale log analysis for detecting suspicious activity in enterprise networks (2013) — 301 citations
• HomeAlone: Co-residency Detection in the Cloud via Side-Channel Analysis (2011) — 358 citations
• HAIL: a high-availability and integrity layer for cloud storage (2009) — 1,160 citations
• Proofs of retrievability: theory and implementation (2009) — 684 citations