Training on large-scale graphs has achieved remarkable results in graph representation
learning, but its cost and storage have attracted increasing concerns. Existing graph …

Contemporary machine learning which involves training large neural networks on massive
datasets faces significant computational challenges. Dataset distillation as a recent …

The sequential recommender (SR) system is a crucial component of modern recommender
systems, as it aims to capture the evolving preferences of users. Significant efforts have …

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 …

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 …

Graph condensation aims to reduce the size of a large-scale graph dataset by synthesizing
a compact counterpart without sacrificing the performance of Graph Neural Networks …

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 …

Dataset distillation aims to synthesize a small number of images per class (IPC) from a large
dataset to approximate full dataset training with minimal performance loss. While effective in …

Recent works in dataset distillation seek to minimize training expenses by generating a
condensed synthetic dataset that encapsulates the information present in a larger real …

Dataset Distillation (DD) is a promising technique to synthesize a smaller dataset that
preserves essential information from the original dataset. This synthetic dataset can serve as …