Person

Guillermo Ortiz Jimenez

I received my BSc. in telecommunications engineering (Valedictorian) from Universidad Politecnica de Madrid (UPM), Spain, in June 2015, and my MSc. in electrical engineering (Best Graduate, cum laude) from Delft University of Technology (TU Delft), The Netherlands, in August 2018. During my studies, I held positions at the Microwaves and Radar Group of UPM during 2015-2016 and at  Philips Healthcare Research in Hamburg (Germany) during summer 2017. I am now working towards my PhD. thesis at the  Signal Processing Laboratory 4 under the supervision of Prof. Pascal Frossard. My current research focuses on understanding deep learning by studying the complex interactions between datasets, architectures and optimization. In my work, I am providing a novel framework to design better and more reliable neural networks that exploit prior knowledge about the world. I am the recipient of a National Award for Excellence in Academic Performance from the Ministry of Education of Spain, a la Caixa Postgraduate Fellowship, and the Best Graduate award both at UPM and TU Delft.

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Related publications (12)

Please note that this is not a complete list of this person’s publications. It includes only semantically relevant works. For a full list, please refer to Infoscience.

The inductive bias of deep learning: Connecting weights and functions

Guillermo Ortiz Jimenez

Years of a fierce competition have naturally selected the fittest deep learning algorithms. Yet, although these models work well in practice, we still lack a proper characterization of why they do so. This poses serious questions about the robustness, trus ...
EPFL2023

A Structured Dictionary Perspective on Implicit Neural Representations

Pascal Frossard, Guillermo Ortiz Jimenez, Gizem Yüce, Beril Besbinar

Implicit neural representations (INRs) have recently emerged as a promising alternative to classical discretized representations of signals. Nevertheless, despite their practical success, we still do not understand how INRs represent signals. We propose a ...
IEEE COMPUTER SOC2022

PRIME: A Few Primitives Can Boost Robustness to Common Corruptions

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Guillermo Ortiz Jimenez, Apostolos Modas

Despite their impressive performance on image classification tasks, deep networks have a hard time generalizing to unforeseen corruptions of their data. To fix this vulnerability, prior works have built complex data augmentation strategies, combining multi ...
SPRINGER INTERNATIONAL PUBLISHING AG2022
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