Alfredo Cuesta-Infante
Alfredo Cuesta-Infante
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Zitiert von
Zitiert von
Modeling tabular data using conditional gan
L Xu, M Skoularidou, A Cuesta-Infante, K Veeramachaneni
Advances in neural information processing systems 32, 2019
Tadgan: Time series anomaly detection using generative adversarial networks
A Geiger, D Liu, S Alnegheimish, A Cuesta-Infante, K Veeramachaneni
2020 ieee international conference on big data (big data), 33-43, 2020
SteganoGAN: High capacity image steganography with GANs
KA Zhang, A Cuesta-Infante, L Xu, K Veeramachaneni
arXiv preprint arXiv:1901.03892, 2019
Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications
JM Górriz, J Ramírez, A Ortíz, FJ Martinez-Murcia, F Segovia, J Suckling, ...
Neurocomputing 410, 237-270, 2020
ATM: A distributed, collaborative, scalable system for automated machine learning
T Swearingen, W Drevo, B Cyphers, A Cuesta-Infante, A Ross, ...
2017 IEEE international conference on big data (big data), 151-162, 2017
glUCModel: A monitoring and modeling system for chronic diseases applied to diabetes
JI Hidalgo, E Maqueda, JL Risco-Martín, A Cuesta-Infante, JM Colmenar, ...
Journal of biomedical informatics 48, 183-192, 2014
Robust invisible video watermarking with attention
KA Zhang, L Xu, A Cuesta-Infante, K Veeramachaneni
arXiv preprint arXiv:1909.01285, 2019
Learning vine copula models for synthetic data generation
Y Sun, A Cuesta-Infante, K Veeramachaneni
Proceedings of the aaai conference on artificial intelligence 33 (01), 5049-5057, 2019
Modeling glycemia in humans by means of grammatical evolution
JI Hidalgo, JM Colmenar, JL Risco-Martin, A Cuesta-Infante, E Maqueda, ...
Applied Soft Computing 20, 40-53, 2014
Mobile robot path planning using a QAPF learning algorithm for known and unknown environments
U Orozco-Rosas, K Picos, JJ Pantrigo, AS Montemayor, A Cuesta-Infante
IEEE Access 10, 84648-84663, 2022
Learning representations for log data in cybersecurity
I Arnaldo, A Cuesta-Infante, A Arun, M Lam, C Bassias, ...
Cyber Security Cryptography and Machine Learning: First International …, 2017
Bayesian capsule networks for 3D human pose estimation from single 2D images
I Ramirez, A Cuesta-Infante, E Schiavi, JJ Pantrigo
Neurocomputing 379, 64-73, 2020
Bivariate empirical and n-variate archimedean copulas in estimation of distribution algorithms
A Cuesta-Infante, R Santana, JI Hidalgo, C Bielza, P Larrañaga
IEEE Congress on Evolutionary Computation, 1-8, 2010
Modeling tabular data using conditional gan. arXiv 2019
L Xu, M Skoularidou, A Cuesta-Infante, K Veeramachaneni
arXiv preprint arXiv:1907.00503 1, 1907
Convolutional neural networks for computer vision-based detection and recognition of dumpsters
I Ramirez, A Cuesta-Infante, JJ Pantrigo, AS Montemayor, JL Moreno, ...
Neural Computing and Applications 32 (17), 13203-13211, 2020
Lightweight tracking-by-detection system for multiple pedestrian targets
B Lacabex, A Cuesta-Infante, AS Montemayor, JJ Pantrigo
Integrated computer-aided engineering 23 (3), 299-311, 2016
Steganogan: Pushing the limits of image steganography
KA Zhang, A Cuesta-Infante, K Veeramachaneni
arXiv preprint arXiv:1901.03892 2, 2019
Sample, estimate, tune: Scaling bayesian auto-tuning of data science pipelines
A Anderson, S Dubois, A Cuesta-Infante, K Veeramachaneni
2017 IEEE International Conference on Data Science and Advanced Analytics …, 2017
Modeling tabular data using conditional GAN. 2019
L Xu, M Skoularidou, A Cuesta-Infante, K Veeramachaneni
URL: https://arxiv. org/abs/1907 503, 1907
Copula graphical models for wind resource estimation
K Veeramachaneni, A Cuesta-Infante, UM O'Reilly
Proceedings of the 24th International Conference on Artificial Intelligence …, 2015
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