Understanding deep learning is increasingly emergent as it penetrates more and more into
industry and science. In recent years, a research line from Fourier analysis sheds light on …

We present a method of generating diverse collections of neural cellular automata (NCA) to
design video game levels. While NCAs have so far only been trained via supervised …

Abstract Machine learning has been implemented in medical applications, especially in
classification models to support diagnosis. In dermatology, it is of great relevance, due to the …

Efforts to improve the learning abilities of neural networks have focused mostly on the role of
optimization methods rather than on weight initializations. Recent findings, however …

Oral health represents an essential component in the quality of life of people, being a
determinant factor in general health since it may affect the risk of suffering other conditions …

The development of methods to guide the design of neural networks is an important open
challenge for deep learning theory. As a paradigm for principled neural architecture design …

The remarkable performance of overparameterized deep neural networks (DNNs) must
arise from an interplay between network architecture, training algorithms, and structure in …

Data generated by cars is growing at an unprecedented scale. As cars gradually become
part of the Internet of Things (IoT) ecosystem, several stakeholders discover the value of in …

The effects of distracted driving are one of the main causes of deaths and injuries on US
roads. According to the National Highway Traffic Safety Administration (NHTSA), among the …

In this work, we analyze the role of the network architecture in shaping the inductive bias of
deep classifiers. To that end, we start by focusing on a very simple problem, ie, classifying a …