I am a PhD student in Machine Learning at the University of Cambridge under the supervision of Dr. José Miguel Hernández-Lobato. I’m interested in Bayesian deep learning, representation learning, uncertainty in machine learning and information theory. I graduated from the University of Zaragoza in 2018 with an honorary distinction (“premio extraordinario”) in Telecommunications Engineering (EE/CS). I was awarded an MPhil in Machine Learning with distinction by the University of Cambridge in 2019. I also do freelance engineering consulting and am a co-founder of arisetech.es. Bellow are links to some of my recent work.


Bayesian Deep Learning via Subnetwork Inference
E. Daxberger, E. Nalisnick, J. U. Allingham, J. Antorán and J. M. Hernández-Lobato.
38th International Conference on Machine Learning (ICML), 2021.
[Paper], [Poster]

Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
U. B., J. Antorán, Y. Z., Q. V. L., P. S., R. F., G. G. M., R. K., J. S., O. T., L. N., R. C., A. W. and A. X.
4th AAAI / ACM conference on Artificial Intelligence, Ethics, and Society (AIES), 2021.

Depth Uncertainty in Neural Networks
J. Antorán, J. U. Allingham and J. M. Hernández-Lobato.
34th Conference on Neural Information Processing Systems (NeurIPS), Vancouver, Canada. 2020.
[Paper], [Poster], [bibtex], [code]

Getting a CLUE: A Method for Explaining Uncertainty Estimates
J. Antorán, U. Bhatt, T. Adel, A. Weller and J. M. Hernández-Lobato.
Oral presentation at The Ninth International Conference on Learning Representations (ICLR), 2021.
[Paper], [Poster], [bibtex], [code]

Variational Depth Search in ResNets
J. Antorán, J. U. Allingham and J. M. Hernández-Lobato.
Contributed talk at 1st Workshop on Neural Architecture Search at ICLR 2020.
[Paper], [Poster], [bibtex], [code]


Understanding Uncertainty in Bayesian Neural Networks
J. Antorán
MPhil Thesis (Awarded Distinction)
[Thesis], [bibtex], [Poster]

Uncertainty in Bayesian Neural Networks (github repo)
J. Antorán and E. Markou.
Presented poster at the Workshop on The Mathematics of Deep Learning and Data Science, The Isaac Newton Institute for Mathematical Sciences, Cambridge, UK. 2019.
[Poster], [Code]


Disentangling in Variational Autoencoders with Natural Clustering
J. Antorán and A. Miguel.
Successfully defended Bachelor’s Thesis at the University of Zaragoza (9.8/10, Honorary Distinction).
Accepted as an oral presentation at the 18th IEEE International Conference on Machine Learning and Applications - ICMLA 2019, Boca Raton, Florida, USA. 2019.
[Paper], [bibtex], [Thesis (Spanish)]

FELIX DAQ Integration Test Tool for the ATLAS experiment at CERN.
J. Antorán and J. Schumacher.
Work done during a Summer studentship at CERN.
[Technical Report], [bibtex]