Neural implicit surfaces are a promising tool for geometry processing that represent a solid
object as the zero level set of a neural network. Usually trained to approximate a signed …

Unsupervised anomaly detection is a challenging computer vision task, in which 2D-based
anomaly detection methods have been extensively studied. However, multimodal anomaly …

Recently, due to the popularity of deep neural networks and other methods whose training
typically relies on the optimization of an objective function, and due to concerns for data …

Novelty Detection (ND) plays a crucial role in machine learning by identifying new or unseen
data during model inference. This capability is especially important for the safe and reliable …

In recent years, there have been significant improvements in various forms of image outlier
detection. However, outlier detection performance under adversarial settings lags far behind …

This thesis explores the characteristics and applications of Lipschitz networks in machine
learning tasks. First, the framework of" optimization as a layer" is presented, showcasing …

Designing control policies whose performance level is guaranteed to remain above a given
threshold in a span of environments is a critical feature for the adoption of reinforcement …