Recent success of deep learning is largely attributed to the sheer amount of data used for
training deep neural networks. Despite the unprecedented success, the massive data …

Abstract Model-based deep learning has achieved astounding successes due in part to the
availability of large-scale real-world data. However, processing such massive amounts of …

Offline model-based optimization aims to find a design that maximizes a property of interest
using only an offline dataset, with applications in robot, protein, and molecule design …

Dataset Distillation is used to create a concise, yet informative, synthetic dataset that can
replace the original dataset for training purposes. Some leading methods in this domain …

With the increasing demands of training graph neural networks (GNNs) on large-scale
graphs, graph data condensation has emerged as a critical technique to relieve the storage …

The success of deep learning relies heavily on large and diverse datasets, but the storage,
preprocessing, and training of such data present significant challenges. To address these …

Time series data has been demonstrated to be crucial in various research fields. The
management of large quantities of time series data presents challenges in terms of deep …

Deep learning has made remarkable progress recently, largely due to the availability of
large, well-labeled datasets. However, the training on such datasets elevates costs and …

While dataset condensation effectively enhances training efficiency, its application in on-
device scenarios brings unique challenges. 1) Due to the fluctuating computational …

Problem: Detecting COVID-19 from chest X-ray (CXR) images has become one of the fastest
and easiest methods for detecting COVID-19. However, the existing methods usually use …