Derivatives play a critical role in computational statistics, examples being Bayesian
inference using Hamiltonian Monte Carlo sampling and the training of neural networks …

Inverse problems in statistical physics are motivated by the challenges of 'big data'in
different fields, in particular high-throughput experiments in biology. In inverse problems, the …

Deep learning frameworks have often focused on either usability or speed, but not both.
PyTorch is a machine learning library that shows that these two goals are in fact compatible …

The performance of the ATLAS muon trigger system is evaluated with proton-proton ($ pp $)
and heavy-ion (HI) collision data collected in Run 2 during 2015-2018 at the Large Hadron …

DOLFINx is the next generation problem solving environment from the FEniCS Project; it
provides an expressive and performant environment for solving partial differential equations …

Modern rendering systems are confronted with a dauntingly large and growing set of
requirements: in their pursuit of realism, physically based techniques must increasingly …

The precision of the cosmological data allows us to accurately approximate the predictions
for cosmological observables by Taylor expanding up to a low order the dependence on the …

This paper presents an implementation of model predictive control (MPC) to determine
ground reaction forces for a torque-controlled quadruped robot. The robot dynamics are …

The rising popularity of intelligent mobile devices and the daunting computational cost of
deep learning-based visual recognition models call for efficient on-device inference …

The iterative closest point (ICP) algorithm and its variants are a fundamental technique for
rigid registration between two point sets, with wide applications in different areas from …