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Found 8 papers, 3 papers with code
Expert Router: Orchestrating Efficient Language Model Inference through Prompt Classification
Large Language Models (LLMs) have experienced widespread adoption across scientific and industrial domains due to their versatility and utility for diverse tasks.
Application-Oriented Benchmarking of Quantum Generative Learning Using QUARK
Benchmarking of quantum machine learning (QML) algorithms is challenging due to the complexity and variability of QML systems, e. g., regarding model ansatzes, data sets, training techniques, and hyper-parameters selection.
Optimization of Robot Trajectory Planning with Nature-Inspired and Hybrid Quantum Algorithms
We solve robot trajectory planning problems at industry-relevant scales.
Quantum Computing: Towards Industry Reference Problems
The complexity is increasing rapidly in many areas of the automotive industry.
Emerging Technologies
Task-parallel Analysis of Molecular Dynamics Trajectories
We also provide a quantitative performance analysis of the different algorithms across the three frameworks.
Distributed, Parallel, and Cluster Computing
Deep Learning in the Automotive Industry: Applications and Tools
In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision.
Algebraic multigrid support vector machines
The support vector machine is a flexible optimization-based technique widely used for classification problems.
Hadoop on HPC: Integrating Hadoop and Pilot-based Dynamic Resource Management
High-performance computing platforms such as supercomputers have traditionally been designed to meet the compute demands of scientific applications.
Distributed, Parallel, and Cluster Computing
