Deep neural networks are at the forefront of machine learning research. However, despite
achieving impressive performance on complex tasks, they can be very sensitive: Small …

Abstract Domain adaptation for semantic image segmentation is very necessary since
manually labeling large datasets with pixel-level labels is expensive and time consuming …

Advances in machine learning have led to broad deployment of systems with impressive
performance on important problems. Nonetheless, these systems can be induced to make …

We provide a complete characterisation of the phenomenon of adversarial examples-inputs
intentionally crafted to fool machine learning models. We aim to cover all the important …

In this work, we argue that the search for Artificial General Intelligence should start from a
much lower level than human-level intelligence. The circumstances of intelligent behavior in …

In autonomous driving, learning a segmentation model that can adapt to various
environmental conditions is crucial. In particular, copying with severe illumination changes is …

The key challenge in processing point clouds lies in the inherent lack of ordering and
irregularity of the 3D points. By relying on perpoint multi-layer perceptions (MLPs), most …

Artificial neural networks are deep machine learning models that excel at complex artificial
intelligence tasks by abstracting concepts through multiple layers of feature extraction …

Despite the recent success of deep learning, the mammalian brain is still unrivaled when it
comes to interpreting complex, high-dimensional data streams like visual, auditory and …

A propagação e produção das notícias falsas são um problema atual e perigoso, que pode
atingir as pessoas com consequências terríveis. Elas podem influenciar um grande número …