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

The power of DNNs relies heavily on the quantity and quality of training data. However,
collecting and annotating data on a large scale is often expensive and time-consuming. To …

The ultimate goal of Dataset Distillation is to synthesize a small synthetic dataset such that a
model trained on this synthetic set will perform equally well as a model trained on the full …

Instruction tuning plays a critical role in aligning large language models (LLMs) with human
preference. Despite the vast amount of open instruction datasets, naively training a LLM on …

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 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 …

Training state-of-the-art (SOTA) deep models often requires extensive data, resulting in
substantial training and storage costs. To address these challenges, dataset condensation …

In recent years there has been significant progress in the development of text-to-image
generative models. Evaluating the quality of the generative models is one essential step in …

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 (DD) is a prominent technique that encapsulates knowledge from a large-
scale original dataset into a small synthetic dataset for efficient training. Meanwhile, Pre …