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

State-of-the-art deep neural networks are trained with large amounts (millions or even
billions) of data. The expensive computation and memory costs make it difficult to train them …

The promise of active learning (AL) is to reduce labelling costs by selecting the most
valuable examples to annotate from a pool of unlabelled data. Identifying these examples is …

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 …

Despite the substantial progress of active learning for image recognition, there still lacks an
instance-level active learning method specified for object detection. In this paper, we …

Artificial intelligence and machine learning (AI/ML) can be used to automatically analyze
large image datasets. One valuable application of this approach is estimation of plant trait …

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 …

Active learning is a promising alternative to alleviate the issue of high annotation cost in the
computer vision tasks by consciously selecting more informative samples to label. Active …

Given the large-scale data and the high annotation cost, pretraining-finetuning becomes a
popular paradigm in multiple computer vision tasks. Previous research has covered both the …

Active learning (AL) is to design label-efficient algorithms by labeling the most
representative samples. It reduces annotation cost and attracts increasing attention from the …