In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially
outperforms training from scratch. When using fine-tuning, the underlying assumption is that …

Learning efficient predictive models in dynamic environments requires taking into account
the continuous changing nature of phenomena generating the data streams, known in …

We present a taxonomy of the Artificial Intelligence (AI) methods currently applied for
algorithmic music composition. The area known as algorithmic music composition concerns …

In marine research, image data sets from the same area but collected at different times allow
seafloor fauna communities to be monitored over time. However, ongoing technological …

Abstract Pre-trained Language Models (LMs) exhibit strong zero-shot and in-context
learning capabilities; however, their behaviors are often difficult to control. By utilizing …

In this chapter, we focus on modern parameter-based methods: the pre-training and fine-
tuning approach. We will also step into deep transfer learning starting from this chapter. In …

Transfer learning has recently shown significant performance across various tasks involving
deep neural networks. In these transfer learning scenarios, the prior distribution for …

In recent times, there has been increasing awareness of employee behaviour prediction in
healthcare, trade, and industry systems worldwide and its value on returns and profits of …

随着声音艺术等垂直领域基础模型的迅速发展, 人工智能与音乐创作表演呈现愈发融合的趋势.
面向音乐创演的流程与需求, 提出新型音乐创演框架——**行音乐系统, 该系统基于**行系统 …

The remarkable advancements in deep learning methodologies over recent years can be
attributed to the availability of large, high-quality labeled datasets, intricate network …