Ablation studies provide insights into the relative contribution of different architectural and
regularization components to machine learning models' performance. In this paper, we …

Bringing together a number of cutting-edge technologies that range from storing extremely
large volumes of data all the way to developing scalable machine learning and deep …

Artificial intelligence (AI), and especially its sub-field of Machine Learning (ML), are
impacting the daily lives of everyone with their ubiquitous applications. In recent years, AI …

AI integration is revolutionizing the landscape of HPC simulations, enhancing the
importance, use, and performance of AI-driven HPC workflows. This paper surveys the …

When there is a need to make an ultimate decision about the unique features of big data
platforms, one should note that they have configurable parameters. Apache Spark is an …

Understanding the efficacy of a method requires ablation experiments. Current Machine
Learning (ML) workflows emphasize the vertical scaling of large models with paradigms …

Research is increasingly showing the tremendous vulnerability in machine learning models
to seemingly undetectable adversarial inputs. One of the current limitations in adversarial …

In recent years, machine-learning methods have become increasingly important for the
experiments at the Large Hadron Collider (LHC). They are utilised in everything from trigger …

Modern neural networks require long training to reach decent performance on massive
datasets. One common approach to speed up training is model parallelization, where large …

Hyperparameter optimization (HO) is a must to figure out to what extent can a specific
configuration of hyperparameters contribute to the performance of a machine learning task …