In this paper, we present a comprehensive review of the imbalance problems in object
detection. To analyze the problems in a systematic manner, we introduce a problem-based …

With the rise of big data, the quality of datasets has become a crucial factor affecting the
performance of machine learning models. High-quality datasets are essential for the …

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 …

Widely observed neural scaling laws, in which error falls off as a power of the training set
size, model size, or both, have driven substantial performance improvements in deep …

Over recent years, an increasing amount of compute and data has been poured into training
large language models (LLMs), usually by doing one-pass learning on as many tokens as …

Training machine learning models on massive datasets incurs substantial computational
costs. To alleviate such costs, there has been a sustained effort to develop data-efficient …

Although deep learning has made great progress in recent years, the exploding economic
and environmental costs of training neural networks are becoming unsustainable. To …

To improve the efficiency and sustainability of learning deep models, we propose CREST,
the first scalable framework with rigorous theoretical guarantees to identify the most valuable …

We propose a novel pool-based Active Learning frame-work constructed on a sequential
Graph Convolution Net-work (GCN). Each image's feature from a pool of data rep-resents a …

A desirable data selection algorithm can efficiently choose the most informative samples to
maximize the utility of limited annotation budgets. However, current approaches …