Pablo M. Olmos
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Research interests
Probabilistic Machine Learning and efficient inference methods
Deep probabilistic models
Medical data modeling and applications in health
Latest news!
I’m excited to announce that my project, “THAI: Towards Humble and Discoverable AI,” has been awarded a grant by the FBBVA Leonardo Program. This year, only 57 out of 1,423 projects were selected (a 4% acceptance rate), making this recognition even more special. We are currently looking for a post-doctoral researcher to join our team and contribute to the project.
Diffusion X-ray image denoising. Daniel Sanderson, Pablo M. Olmos, Carlos Fernández Del Cerro, Manuel Desco, Mónica Abella
Decoupling Feature Extraction and Classification Layers for Calibrated Neural Networks. Mikkel Jordahn, Pablo M. Olmos
Training Implicit Generative Models via an Invariant Statistical Loss. Jose Manuel de Frutos, Pablo M. Olmos, Manuel Alberto Vázquez, Joaquín Míguez
Efficient local linearity regularization to overcome catastrophic overfitting. Elias Abad Rocamora, Fanghui Liu, Grigorios G. Chrysos, Pablo M. Olmos, Volkan Cevher
Variational Mixture of HyperGenerators for Learning Distributions Over Functions. Batuhan Koyuncu, Pablo Sanchez-Martin, Ignacio Peis, Pablo M. Olmos, Isabel Valera
Contact me
Address: Universidad Carlos III de Madrid, Dpto. de Teoría de la Señal y Comunicaciones, Avenida de la Universidad 30, 28911 Leganés, Spain
Office: 4.2.A.08
Office phone: +34916248875
email: pamartin@ing.uc3m.es