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Ramiro Camino
Ramiro Camino
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Title
Cited by
Cited by
Year
Frontrunner Jones and the Raiders of the Dark Forest: An Empirical Study of Frontrunning on the Ethereum Blockchain
C Ferreira Torres, R Camino
arXiv e-prints, arXiv: 2102.03347, 2021
107*2021
Generating Multi-Categorical Samples with Generative Adversarial Networks
R Camino, C Hammerschmidt, R State
arXiv preprint arXiv:1807.01202, 2018
902018
Improving Missing Data Imputation with Deep Generative Models
RD Camino, CA Hammerschmidt, R State
arXiv preprint arXiv:1902.10666, 2019
482019
Finding suspicious activities in financial transactions and distributed ledgers
RD Camino, R State, L Montero, P Valtchev
2017 IEEE International Conference on Data Mining Workshops (ICDMW), 787-796, 2017
452017
A Data Science Approach for Detecting Honeypots in Ethereum
R Camino, CF Torres, M Baden, R State
2020 IEEE International Conference on Blockchain and Cryptocurrency (ICBC), 1-9, 2020
28*2020
Working with Deep Generative Models and Tabular Data Imputation
R Camino, C Hammerschmidt
72020
Oversampling Tabular Data with Deep Generative Models: Is it worth the effort?
R Camino, C Hammerschmidt
''I Can't Believe It's Not Better!''NeurIPS 2020 workshop, 2020
42020
On non-parametric models for detecting outages in the mobile network
E Falk, R Camino, R State, VK Gurbani
2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM …, 2017
42017
GAN Applications with Discrete Data
RD Camino
12018
Assessing the Electromagnetic Field Exposure of 5G Transmitters: Challenges & Solutions
J Estrada, E Pardo, R Camino, S Faye
Proceedings of the 20th ACM International Symposium on Mobility Management …, 2022
2022
Low-Emission Driving Assistants: Experience from MODALES
R Camino, S Faye, N Dimokas, D Margaritis
14th ITS European Congress, Toulouse, France, 2022
2022
Machine Learning Techniques for Suspicious Transaction Detection and Analysis
RD Camino
University of Luxembourg, Luxembourg, Luxembourg, 2020
2020
Minority Class Oversampling for Tabular Data with Deep Generative Models
R Camino, C Hammerschmidt, R State
arXiv preprint arXiv:2005.03773, 2020
2020
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