Detecting anomalous samples in set data is a problem attracting increased interest due to
novel modalities, such as point-cloud data produced by lidars. Novel methods including …

We present ObSuRF, a method which turns a single image of a scene into a 3D model
represented as a set of Neural Radiance Fields (NeRFs), with each NeRF corresponding to …

There has been a recent explosion in research into machine-learning-based generative
modeling to tackle computational challenges for simulations in high energy physics (HEP) …

Generative models are typically trained on grid-like data such as images. As a result, the
size of these models usually scales directly with the underlying grid resolution. In this paper …

Generative modeling of set-structured data, such as point clouds, requires reasoning over
local and global structures at various scales. However, adopting multi-scale frameworks for …

This work addresses one-shot set and graph generation, and, more specifically, the
parametrization of probabilistic decoders that map a vector-shaped prior to a distribution …

In high energy physics (HEP), machine learning methods have emerged as an effective way
to accurately simulate particle collisions at the Large Hadron Collider (LHC). The message …

After observing a snapshot of a social network, a link prediction (LP) algorithm identifies
node pairs between which new edges will likely materialize in future. Most LP algorithms …

Human mobility clustering is an important problem for understanding human mobility
behaviors (eg, work and school commutes). Existing methods typically contain two steps …

La adopción de la inteligencia artificial (IA) está revolucionando el marketing digital en las
empresas al permitir una personalización precisa, una automatización eficiente y un …