The groundbreaking success of deep learning in many real-world tasks has triggered an
intense effort to theoretically understand the power and limitations of deep learning in the …

This article deals with the IT security of connectionist artificial intelligence (AI) applications,
focusing on threats to integrity, one of the three IT security goals. Such threats are for …

Inverse molecular design involves algorithms that sample molecules with specific target
properties from a multitude of candidates and can be posed as an optimization problem …

Computer-assisted design of small molecules has experienced a resurgence in academic
and industrial interest due to the widespread use of data-driven techniques such as deep …

We derive machine learning algorithms from discretized Euclidean field theories, making
inference and learning possible within dynamics described by quantum field theory …

Recent years have seen an explosion of work and interest in text‐to‐3D shape generation.
Much of the progress is driven by advances in 3D representations, large‐scale pretraining …

We perform accurate numerical experiments with fully connected one hidden layer neural
networks trained with a discretized Langevin dynamics on the MNIST and CIFAR10 …

In this paper, we present the first study that compares different models of Bayesian neural
networks (BNNs) to predict the posterior distribution of the cosmological parameters directly …

Geometric deep learning has demonstrated a great potential in non-Euclidean data
analysis. The incorporation of geometric insights into learning architecture is vital to its …

Accurately predicting the future trajectory and behavior of traffic participants is crucial for the
maneuvers of self‐driving vehicles. Many existing works employed a learning‐based …