Nicholas Carlini
Impact in
- Signal Processing top 1%
- Advanced Malware Detection Techniques
- Artificial Intelligence top 0.5%
- Adversarial Robustness in Machine Learning
- Anomaly Detection Techniques and Applications
- Privacy-Preserving Technologies in Data
- Security and Verification in Computing
- Domain Adaptation and Few-Shot Learning
Papers in
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- Adversarial Robustness in Machine Learning 14
- Anomaly Detection Techniques and Applications 6
- Domain Adaptation and Few-Shot Learning 4
- Privacy-Preserving Technologies in Data 4
- Cryptography and Data Security 3
- Security and Verification in Computing 3
- Topic Modeling 3
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- Advanced Malware Detection Techniques 8
- Co-authors
- David Wagner (7 shared papers)Florian Tramèr (9 shared papers)Andreas Terzis (2 shared papers)Milad Nasr (5 shared papers)Shuang Song (1 shared paper)Steve Chien (1 shared paper)David Berthelot (3 shared papers)А.В. Куракин (3 shared papers)
- Journals
- arXiv (Cornell University) (2 papers)Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (1 paper)Neural Information Processing Systems (2 papers)USENIX Security Symposium (3 papers)Repository for Publications and Research Data (ETH Zurich) (1 paper)
- Partner nations
- United StatesSwitzerlandUnited Kingdom
In The Last Decade
Nicholas Carlini
29 papers receiving 1.7k citations
Nicholas Carlini's Hit Papers
Peers
Comparison fields: 5 of 94
- Signal Processing 566
- Artificial Intelligence 1.6k
- Health Informatics 37
- Computer Vision and Pattern Recognition 357
- Hardware and Architecture 107
Countries citing papers authored by Nicholas Carlini
This map shows the geographic impact of Nicholas Carlini's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nicholas Carlini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nicholas Carlini more than expected).
Fields of papers citing papers by Nicholas Carlini
This network shows the impact of papers produced by Nicholas Carlini. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nicholas Carlini. The network helps show where Nicholas Carlini may publish in the future.
Co-authors
The 25 scholars most cited alongside Nicholas Carlini, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Adversarial Examples Are Not Easily Detected Hit paper breakdown → | 2017 | 608 |
| 2 | Membership Inference Attacks From First Principles Hit paper breakdown → | 2022 | 182 |
| 3 | ROP is still dangerous: breaking modern defenses | 2014 | 168 |
| 4 | ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring | 2020 | 165 |
| 5 | Hidden voice commands | 2016 | 161 |
| 6 | Deduplicating Training Data Makes Language Models Better Hit paper breakdown → | 2022 | 147 |
| 7 | Adversarial Example Defense: Ensembles of Weak Defenses are not Strong | 2017 | 94 |
| 8 | 2020 | 57 | |
| 9 | An evaluation of the Google Chrome extension security architecture | 2012 | 42 |
| 10 | 2022 | 32 | |
| 11 | FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence | 2020 | 28 |
| 12 | 2024 | 26 | |
| 13 | Adversarial Examples Are a Natural Consequence of Test Error in Noise | 2019 | 22 |
| 14 | Ground-Truth Adversarial Examples | 2018 | 22 |
| 15 | 2021 | 21 | |
| 16 | High-Fidelity Extraction of Neural Network Models. | 2019 | 12 |
| 17 | 2023 | 11 | |
| 18 | Measuring Robustness to Natural Distribution Shifts in Image Classification | 2020 | 11 |
| 19 | Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations | 2020 | 8 |
| 20 | Prototypical Examples in Deep Learning: Metrics, Characteristics, and Utility | 2018 | 4 |
About Nicholas Carlini
Nicholas Carlini is a scholar working on Artificial Intelligence, Signal Processing, Computer Vision and Pattern Recognition, Information Systems and Computer Networks and Communications, having authored 34 papers that have together received 1.8k indexed citations. Recurring topics across this work include Adversarial Robustness in Machine Learning (14 papers), Advanced Malware Detection Techniques (8 papers), Anomaly Detection Techniques and Applications (6 papers), Domain Adaptation and Few-Shot Learning (4 papers), Privacy-Preserving Technologies in Data (4 papers), Cryptography and Data Security (3 papers), Security and Verification in Computing (3 papers) and Topic Modeling (3 papers). The work is most often cited by research in Signal Processing (566 citations), Artificial Intelligence (1.6k citations), Health Informatics (37 citations), Computer Vision and Pattern Recognition (357 citations) and Hardware and Architecture (107 citations). Nicholas Carlini has collaborated with scholars based in United States, Switzerland and United Kingdom. Frequent co-authors include David Wagner, Florian Tramèr, Andreas Terzis, Milad Nasr, Shuang Song, Steve Chien, David Berthelot, А.В. Куракин, Colin Raffel and Kihyuk Sohn. Their work appears in journals such as arXiv (Cornell University), Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, Neural Information Processing Systems, USENIX Security Symposium and Repository for Publications and Research Data (ETH Zurich).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.