Publications
You can find most of my articles on my Google Scholar profile.
Current Highlighs
Paula G. Duran, Pere Gilabert, Santi Seguí, and Jordi Vitrià. 2024. Overcoming Diverse Undesired Effects in Recommender Systems: A Deontological Approach. ACM Trans. Intell. Syst. Technol. Just Accepted (February 2024). https://doi.org/10.1145/3643857
Arnau Quindós, Pablo Laiz, Jordi Vitrià, Santi Seguí, Self-supervised out-of-distribution detection in wireless capsule endoscopy images, Artificial Intelligence in Medicine, 2023, 102606, ISSN 0933-3657, https://doi.org/10.1016/j.artmed.2023.102606
Laiz, P., Vitrià, J., Gilabert, P., Wenzek, H., Malagelada, C., Watson, A. J., & Seguí, S. (2023). Anatomical landmarks localization for capsule endoscopy studies. Computerized Medical Imaging and Graphics, 102243.
García, C., Mora, O., Pérez-Aragüés, F. et al. CatLC: Catalonia Multiresolution Land Cover Dataset. Sci Data 9, 554 (2022). https://doi.org/10.1038/s41597-022-01674-y
Brando, Axel, et al. Deep Non-Crossing Quantiles through the Partial Derivative. International Conference on Artificial Intelligence and Statistics. PMLR, 2022.
Pascual, Guillem, et al. Time-based Self-supervised Learning for Wireless Capsule Endoscopy. Computers in Biology and Medicine, Volume 146, 2022.
Alvaro Parafita, and Jordi Vitrià. “Deep Causal Graphs for Causal Inference, Black-Box Explainability and Fairness.” Artificial Intelligence Research and Development: Proceedings of the 23rd International Conference of the Catalan Association for Artificial Intelligence. Vol. 339. IOS Press, 2021.
J. Mur, J.Vitrià. What’s on the telly? Causality for recommender systems in public-service media corporations, Causal Data Science Meeting 2021, November 15–16, 2021. The workshop will be online, jointly organized by Maastricht University and Copenhagen Business School.
M. Pedemonte, J. Vitrià, and A. Parafita. “Algorithmic Causal Effect Identification with
causaleffect.” arXiv preprint arXiv:2107.04632 (2021).J. Mena, O. Pujol, and J. Vitrià. 2021. A Survey on Uncertainty Estimation in Deep Learning Classification Systems from a Bayesian Perspective. ACM Comput. Surv. 54, 9, DOI:https://doi.org/10.1145/3477140
Duran, P. G., Karatzoglou, A., Vitrià, J., Xin, X., & Arapakis, I. (2021). Graph Convolutional Embeddings for Recommender System, in IEEE Access.
Parafita, Á., & Vitrià, J. (2021). Deep Causal Graphs for Causal Inference, Black-Box Explainability and Fairness. 2ond International Conference of the Catalan Association for Artificial Intelligence, 2021.
Parafita, Á., & Vitrià, J. (2020). Causal Inference with Deep Causal Graphs.. arXiv e-prints.
Laiz, P., Vitrià, J., Wenzek, H., Malagelada, C., Azpiroz, F., & Seguí, S. (2020). WCE polyp detection with triplet based embeddings.. Computerized Medical Imaging and Graphics, 86, 101794.
J. Mena, O. Pujol and J. Vitrià, Uncertainty-based Rejection Wrappers for Black-box Classifiers, in IEEE Access, doi: 10.1109/ACCESS.2020.2996495.
J.Vitrià. ¿Que sabe usted de su robot aspirador? . The Conversation. 2020.
José Mena Roldán, Oriol Pujol Vila and Jordi Vitrià Marca. Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability. ACM FAT* Conference, 2020.
Axel Brando, Jose A Rodriguez, Jordi Vitria, Alberto Rubio. Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians. NeurIPS, 2019.
P. Laiz, S.seguí, J.Vitrià. Using the triplet loss for domain adaptation in WCE. ICCV 2019 Workshop on Visual Recognition for Medical Images, Seoul, 2019.
Álvaro Parafita, Jordi Vitrià. Explaining Visual Models by Causal Attribution. 2019 ICCV Workshop on Interpretating and Explaining Visual Artificial Intelligence Models, Seoul, 2019.
José Mena Roldán, Marc Torrent-Moreno, Daniel González Vidal, Laura Portell Penadés, Oriol Pujol Vila and Jordi Vitrià Marca. Analysis of Vocational Education and Training and the labour market in Catalonia. A Data-driven approach. The 4th Workshop on Data Science for Social Good - SoGood 2019.
José Mena, Axel Brando, Oriol Pujol and Jordi Vitrià. Uncertainty estimation for black-box classification models: a use case for sentiment analysis. IbPRIA 2019.
Axel Brando, Jose A. Rodríguez-Serrano, Jordi Vitrià. Detecting Unusual Expense Categoriesfor Financial Advice Apps. 2nd KDD Workshop on Anomaly Detection in Finance, 2019.
Michal Drozdzal, Santiago Segui, Jordi Vitria, Petia Radeva, Carolina Malagelada, Fernando Azpiroz. System and method for sequential image analysis of an in vivo image stream. US Patent App. 10/204,411, 2019.
Axel Brando, Jose A. Rodrıguez-Serrano, Mauricio Ciprian, Roberto Maestre and Jordi Vitrià, Uncertainty Modelling in Deep Networks for Short and Noisy Time Series Forecasting. ECMLPKDD 2018.
Jordi Vitrià. Let’s Open the Black Box of Deep Learning! In Business Intelligence and Big Data, Edited by Zimanyi, Esteban. Springer International Publishing. 2018.
Guillem Pascual, Santi Seguí, Jordi Vitria. Uncertainty Gated Network for Land Cover Segmentation. Poster at DeepGlobe CVPR 2018 workshop.
We (E.Puertas, S.Seguí, O.Pujol and J.Vitrià) have written a book entitled "El Poder de los datos : del big data al aprendizaje profundo" for RBA Editores & National Geographic. The book has already been translated to English and Italian!
We have written a book entitled Introduction to Data Science. A Python Approach to Concepts, Techniques and Applications for Springer. The book, with a large set of Jupyter notebooks , is the companion of a hands-on course on Data Science.
We have presented the result of a collaboration with BBVA D&A in the Workshop on Machine Learning for Spatiotemporal Forecasting as part of the NIPS 2016 conference. Our contribution is entitled "Evaluating uncertainty scores for deep regression networks in financial short time series forecasting".
Seguí, Santi, Michal Drozdzal, Guillem Pascual, Petia Radeva, Carolina Malagelada, Fernando Azpiroz and Jordi Vitrià. “Generic feature learning for wireless capsule endoscopy analysis.” Computers in biology and medicine 79 (2016): 163-172.