A systematic literature review on explainability for machine/deep learning-based software engineering research
The remarkable achievements of Artificial Intelligence (AI) algorithms, particularly in
Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment …
Machine Learning (ML) and Deep Learning (DL), have fueled their extensive deployment …
Unicorn: Reasoning about configurable system performance through the lens of causality
Modern computer systems are highly configurable, with the total variability space sometimes
larger than the number of atoms in the universe. Understanding and reasoning about the …
larger than the number of atoms in the universe. Understanding and reasoning about the …
CAFQA: A classical simulation bootstrap for variational quantum algorithms
Classical computing plays a critical role in the advancement of quantum frontiers in the NISQ
era. In this spirit, this work uses classical simulation to bootstrap Variational Quantum …
era. In this spirit, this work uses classical simulation to bootstrap Variational Quantum …
Runtime prediction of big data jobs: performance comparison of machine learning algorithms and analytical models
Due to the rapid growth of available data, various platforms offer parallel infrastructure that
efficiently processes big data. One of the critical issues is how to use these platforms to …
efficiently processes big data. One of the critical issues is how to use these platforms to …
[HTML][HTML] Anisotropic molecular coarse-graining by force and torque matching with neural networks
MO Wilson, DM Huang - The Journal of Chemical Physics, 2023 - pubs.aip.org
We develop a machine-learning method for coarse-graining condensed-phase molecular
systems using anisotropic particles. The method extends currently available high …
systems using anisotropic particles. The method extends currently available high …
Input sensitivity on the performance of configurable systems an empirical study
Widely used software systems such as video encoders are by necessity highly configurable,
with hundreds or even thousands of options to choose from. Their users often have a hard …
with hundreds or even thousands of options to choose from. Their users often have a hard …
AgileCtrl: a self-adaptive framework for configuration tuning
Software systems increasingly expose performance-sensitive configuration parameters, or
PerfConfs, to users. Unfortunately, the right settings of these PerfConfs are difficult to decide …
PerfConfs, to users. Unfortunately, the right settings of these PerfConfs are difficult to decide …
Programming with neural surrogates of programs
Surrogates, models that mimic the behavior of programs, form the basis of a variety of
development workflows. We study three surrogate-based design patterns, evaluating each …
development workflows. We study three surrogate-based design patterns, evaluating each …
CAMEO: A Causal Transfer Learning Approach for Performance Optimization of Configurable Computer Systems
Modern computer systems are highly configurable, with hundreds of configuration options
that interact, resulting in an enormous configuration space. As a result, optimizing …
that interact, resulting in an enormous configuration space. As a result, optimizing …
Learning input-aware performance models of configurable systems: An empirical evaluation
Modern software-based systems are highly configurable and come with a number of
configuration options that impact the performance of the systems. However, selecting …
configuration options that impact the performance of the systems. However, selecting …