Over the past few years, neural networks have exhibited remarkable results for various
applications in machine learning and computer vision. Weight initialization is a significant …

In modern clinical practice, digital pathology has a crucial role and is increasingly a
technological requirement in the scientific laboratory environment. The advent of whole-slide …

Bayesian optimization provides sample-efficient global optimization for a broad range of
applications, including automatic machine learning, engineering, physics, and experimental …

In this paper, we present a deep autoencoder based energy method (DAEM) for the
bending, vibration and buckling analysis of Kirchhoff plates. The DAEM exploits the higher …

Physics-informed neural networks (PINNs) have recently been very successfully applied for
efficiently approximating inverse problems for partial differential equations (PDEs). We focus …

Physics-informed neural networks (PINNs) have recently been widely used for robust and
accurate approximation of partial differential equations (PDEs). We provide upper bounds …

As the total value of the global financial market outgrew the value of the real economy,
financial institutions created a global web of interactions that embodies systemic risks …

In many real-world scenarios, decision makers seek to efficiently optimize multiple
competing objectives in a sample-efficient fashion. Multi-objective Bayesian optimization …

Standard convolutional neural networks assume a grid structured input is available and
exploit discrete convolutions as their fundamental building blocks. This limits their …

Droplet-based microfluidic devices hold immense potential in becoming inexpensive
alternatives to existing screening platforms across life science applications, such as enzyme …