Document detail
ID
oai:arXiv.org:2406.10843
Topic
Computer Science - Machine Learnin...Author
Polag, Matthias Ivanov, Todor Eichhorn, TimoCategory
Computer Science
Year
2024
listing date
6/19/2024
Keywords
learningAbstract
In the era of Big Data and the growing support for Machine Learning, Deep Learning and Artificial Intelligence algorithms in the current software systems, there is an urgent need of standardized application benchmarks that stress test and evaluate these new technologies.
Relying on the standardized BigBench (TPCx-BB) benchmark, this work enriches the improved BigBench V2 with three new workloads and expands the coverage of machine learning algorithms.
Our workloads utilize multiple algorithms and compare different implementations for the same algorithm across several popular libraries like MLlib, SystemML, Scikit-learn and Pandas, demonstrating the relevance and usability of our benchmark extension.
Polag, Matthias,Ivanov, Todor,Eichhorn, Timo, 2024, Enriching the Machine Learning Workloads in BigBench
UniZero: Generalized and Efficient Planning with Scalable Latent World Models
unizero latent learning
Reconstruction of the Corticospinal Tract in Patients with Motor-Eloquent High-Grade Gliomas Using Multilevel Fiber Tractography Combined with Functional Motor Cortex Mapping
mapping motor-eloquent threshold anisotropy spherical gliomas patients corticospinal tract tractography
