no code implementations • 22 Apr 2024 • Josef Pichlmeier, Philipp Ross, Andre Luckow
Large Language Models (LLMs) have experienced widespread adoption across scientific and industrial domains due to their versatility and utility for diverse tasks.
2 code implementations • 8 Aug 2023 • Florian J. Kiwit, Marwa Marso, Philipp Ross, Carlos A. Riofrío, Johannes Klepsch, Andre Luckow
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.
no code implementations • 8 Jun 2022 • Martin J. A. Schuetz, J. Kyle Brubaker, Henry Montagu, Yannick van Dijk, Johannes Klepsch, Philipp Ross, Andre Luckow, Mauricio G. C. Resende, Helmut G. Katzgraber
We solve robot trajectory planning problems at industry-relevant scales.
no code implementations • 12 Mar 2021 • Andre Luckow, Johannes Klepsch, Josef Pichlmeier
The complexity is increasing rapidly in many areas of the automotive industry.
Emerging Technologies
1 code implementation • 23 Jan 2018 • Ioannis Paraskevakos, Andre Luckow, Mahzad Khoshlessan, George Chantzialexiou, Thomas E. Cheatham, Oliver Beckstein, Geoffrey C. Fox, Shantenu Jha
We also provide a quantitative performance analysis of the different algorithms across the three frameworks.
Distributed, Parallel, and Cluster Computing
no code implementations • 30 Apr 2017 • Andre Luckow, Matthew Cook, Nathan Ashcraft, Edwin Weill, Emil Djerekarov, Bennie Vorster
In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision.
1 code implementation • 16 Nov 2016 • Ehsan Sadrfaridpour, Sandeep Jeereddy, Ken Kennedy, Andre Luckow, Talayeh Razzaghi, Ilya Safro
The support vector machine is a flexible optimization-based technique widely used for classification problems.
no code implementations • 31 Jan 2016 • Andre Luckow, Ioannis Paraskevakos, George Chantzialexiou, Shantenu Jha
High-performance computing platforms such as supercomputers have traditionally been designed to meet the compute demands of scientific applications.
Distributed, Parallel, and Cluster Computing