When selecting data for training large-scale models, standard practice is to filter for
examples that match human notions of data quality. Such filtering yields qualitatively clean …

Dataset pruning aims to construct a coreset capable of achieving performance comparable
to the original full dataset. Most existing dataset pruning methods rely on snapshot-based …

Abstract In Machine Learning (ML), the use of subsets of training data, referred to as
batches, rather than the entire dataset, has been extensively researched to reduce …

In this paper, a framework based on a sparse Mixture of Experts (MoE) architecture is
proposed for the federated learning and application of a distributed classification model in …

In the article at hand neural networks are used to model liquidity in financial markets, under
conic finance settings, in two different contexts. That is, on the one hand this paper illustrates …

Providing high-quality predictions is no longer the sole goal for neural networks. As we live
in an increasingly interconnected world, these models need to match the constraints of …

To address the growing size of AI model training data and the lack of a universal data
selection methodology-factors that significantly drive up training costs--this paper presents …

The escalating climate crisis demands urgent action to mitigate the environmental impact of
energy-intensive technologies, including Artificial Intelligence (AI). Lowering AI's …

Existing dataset pruning techniques primarily focus on classification tasks, limiting their
applicability to more complex and practical tasks like instance segmentation. Instance …

In real-world machine learning (ML) pipelines, datasets are continuously growing. Models
must incorporate this new training data to improve generalization and adapt to potential …