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 …

Multimodal datasets are a critical component in recent breakthroughs such as CLIP, Stable
Diffusion and GPT-4, yet their design does not receive the same research attention as model …

Being able to spot defective parts is a critical component in large-scale industrial
manufacturing. A particular challenge that we address in this work is the cold-start problem …

Dataset condensation aims at reducing the network training effort through condensing a
cumbersome training set into a compact synthetic one. State-of-the-art approaches largely …

Leveraging Large Language Models (LLMs) for recommendation has recently garnered
considerable attention, where fine-tuning plays a key role in LLMs' adaptation. However, the …

Dataset Condensation aims to condense a large dataset into a smaller one while
maintaining its ability to train a well-performing model, thus reducing the storage cost and …

Coreset selection, which aims to select a subset of the most informative training samples, is
a long-standing learning problem that can benefit many downstream tasks such as data …

Contrastive Language-Image Pre-training (CLIP) is an approach that has advanced
research and applications in computer vision, fueling modern recognition systems and …

We study the problem of selecting a subset of big data to train a classifier while incurring
minimal performance loss. We show the connection of submodularity to the data likelihood …

Abstract While Vehicular Ad-hoc Network (VANET) is developed to enable effective vehicle
communication and traffic information exchange, VANET is also vulnerable to different …