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相关论文: Foundation Models for Music: A Survey

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This survey paper explores the transformative influence of frontier AI, foundation models, and Large Language Models (LLMs) in the realm of Intelligent Transportation Systems (ITS), emphasizing their integral role in advancing…

计算与语言 · 计算机科学 2024-01-17 Mohamed R. Shoaib , Heba M. Emara , Jun Zhao

Foundation Models (FMs) such as Large Language Models (LLMs) are reshaping the software industry by enabling FMware, systems that integrate these FMs as core components. In this KDD 2025 tutorial, we present a comprehensive exploration of…

This position paper explores the rapid development of Foundation Models (FMs) in AI and their implications for intelligence and reasoning. It examines the characteristics of FMs, including their training on vast datasets and use of…

人工智能 · 计算机科学 2025-08-05 Alan F. Smeaton

Large Language Models (LLMs) have emerged as a transformative AI paradigm, profoundly influencing daily life through their exceptional language understanding and contextual generation capabilities. Despite their remarkable performance, LLMs…

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach,…

Large language models (LLMs) are becoming increasingly better at a wide range of Natural Language Processing tasks (NLP), such as text generation and understanding. Recently, these models have extended their capabilities to coding tasks,…

机器学习 · 计算机科学 2024-10-23 Nishat Raihan , Mohammed Latif Siddiq , Joanna C. S. Santos , Marcos Zampieri

In recent years, artificial intelligence (AI) has made significant progress in the field of music generation, driving innovation in music creation and applications. This paper provides a systematic review of the latest research advancements…

声音 · 计算机科学 2024-09-06 Yanxu Chen , Linshu Huang , Tian Gou

Recently, large models, or foundation models, have exhibited remarkable performance, profoundly impacting research paradigms in diverse domains. Foundation models, trained on extensive and diverse datasets, provide exceptional…

地球物理 · 物理学 2024-12-30 Qi Liu , Jianwei Ma

Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range of downstream tasks have gained significant interest lately in various deep-learning problems undergoing a paradigm shift with the rise of these…

计算机视觉与模式识别 · 计算机科学 2023-10-31 Bobby Azad , Reza Azad , Sania Eskandari , Afshin Bozorgpour , Amirhossein Kazerouni , Islem Rekik , Dorit Merhof

Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus…

Pre-trained Foundation Models (PFMs) have ushered in a paradigm-shift in Artificial Intelligence, due to their ability to learn general-purpose representations that can be readily employed in a wide range of downstream tasks. While PFMs…

数据库 · 计算机科学 2024-11-13 Pasquale Balsebre , Weiming Huang , Gao Cong , Yi Li

Large Language Model (LLM)-based recommendation systems excel in delivering comprehensive suggestions by deeply analyzing content and user behavior. However, they often inherit biases from skewed training data, favoring mainstream content…

信息检索 · 计算机科学 2026-02-02 Anindya Bijoy Das , Shahnewaz Karim Sakib

Research on large language models has advanced significantly across text, speech, images, and videos. However, multi-modal music understanding and generation remain underexplored due to the lack of well-annotated datasets. To address this,…

声音 · 计算机科学 2024-12-10 Shansong Liu , Atin Sakkeer Hussain , Qilong Wu , Chenshuo Sun , Ying Shan

Large Language Models (LLMs) have demonstrated impressive success across various tasks. Integrating LLMs with Federated Learning (FL), a paradigm known as FedLLM, offers a promising avenue for collaborative model adaptation while preserving…

机器学习 · 计算机科学 2026-02-25 Yebo Wu , Chunlin Tian , Jingguang Li , He Sun , Kahou Tam , Zhanting Zhou , Haicheng Liao , Jing Xiong , Zhijiang Guo , Li Li , Chengzhong Xu

Following its success for vision and text, the "foundation model" (FM) paradigm -- pretraining large models on massive data, then fine-tuning on target tasks -- has rapidly expanded to domains in the sciences, engineering, healthcare, and…

机器学习 · 计算机科学 2025-03-24 Zongzhe Xu , Ritvik Gupta , Wenduo Cheng , Alexander Shen , Junhong Shen , Ameet Talwalkar , Mikhail Khodak

Enhancing the ability of Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) to interpret sheet music is a crucial step toward building AI musicians. However, current research lacks both evaluation benchmarks and…

计算与语言 · 计算机科学 2025-09-29 Zhilin Wang , Zhe Yang , Yun Luo , Yafu Li , Xiaoye Qu , Ziqian Qiao , Haoran Zhang , Runzhe Zhan , Derek F. Wong , Jizhe Zhou , Yu Cheng

Large language models (LLMs) represent a new paradigm for processing unstructured data, with applications across an unprecedented range of domains. In this paper, we address, through two arguments, whether the development and application of…

统计方法学 · 统计学 2026-02-03 Weijie Su

The rise of foundation models has transformed machine learning research, prompting efforts to uncover their inner workings and develop more efficient and reliable applications for better control. While significant progress has been made in…

Recent advances in multimodal large language models (MLLM) for audio music have demonstrated strong capabilities in music understanding, yet symbolic music, a fundamental representation of musical structure, remains unexplored. In this…

多媒体 · 计算机科学 2026-01-30 Meng Yang , Jon McCormack , Maria Teresa Llano , Wanchao Su , Chao Lei

Moral values have deep roots in early civilizations, codified within norms and laws that regulated societal order and the common good. They play a crucial role in understanding the psychological basis of human behavior and cultural…

计算与语言 · 计算机科学 2025-04-07 Lorenzo Zangari , Candida M. Greco , Davide Picca , Andrea Tagarelli