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 …

Massive data is often considered essential for deep learning applications, but it also incurs
significant computational and infrastructural costs. Therefore, dataset pruning (DP) has …

Dataset distillation aims to minimize the time and memory needed for training deep networks
on large datasets, by creating a small set of synthetic images that has a similar …

Measuring Efficiency in neural network system development is an open research problem.
This paper presents an experimental framework to measure the training efficiency of a …

Incremental Learning (IL) deals with learning from continuous streams of data while
minimising catastrophic forgetting. This field of Machine Learning (ML) research has …

This paper proposes a method for hiding the least-important samples during the training of
deep neural networks to increase efficiency, ie, to reduce the cost of training. Using …

In this paper, we study how to acquire labeled data points from a large data pool to enrich a
training set for enhancing supervised machine learning (ML) performance. The state-of-the …

Large-scale, high-quality data are considered an essential factor for the successful
application of many deep learning techniques. Meanwhile, numerous real-world deep …

Deep learning recommendation models (DLRMs) are at the heart of the current e-commerce
industry. However, the amount of training data used to train these large models is growing …

Abstract Contrastive Language-Image Pre-training (CLIP) on large-scale image-caption
datasets learns representations that can achieve remarkable zero-shot generalization …