Deep learning has been widely used for medical image segmentation and a large number of
papers has been presented recording the success of deep learning in the field. A …

We propose a novel method for gradient-based optimization of black-box simulators using
differentiable local surrogate models. In fields such as physics and engineering, many …

Bayesian optimization (BO) is a widely popular approach for the hyperparameter
optimization (HPO) in machine learning. At its core, BO iteratively evaluates promising …

Sign language is the main type of communication of the deaf community. However, most
people do not know this language, which causes communication problems for many people …

Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and
produce unreliable numerical answers. The state-of-the-art to test for floating-point …

A central problem of materials science is to determine whether a hypothetical material is
stable without being synthesized, which is mathematically equivalent to a global …

Testing code for floating-point exceptions is crucial as exceptions can quickly propagate and
produce unreliable numerical answers. The state-of-the-art to test for floating-point …

There is a wave of interest in using unsupervised neural networks for solving differential
equations. The existing methods are based on feed-forward networks,{while} recurrent …

Sample efficiency is one of the key factors when applying policy search to real-world
problems. In recent years, Bayesian Optimization (BO) has become prominent in the field of …

Bayesian optimization (BO) is a widely popular approach for the hyperparameter
optimization (HPO) of machine learning algorithms. At its core, BO iteratively evaluates …