591 citations
- Chang Gung Memorial HospitalTW10 papers
- Shenzhen Technology UniversityCN9 papers
- National Institutes of Health Clinical CenterUS8 papers
- Linkou Chang Gung Memorial HospitalTW6 papers
- National Institutes of HealthUS6 papers
- Beijing University of Posts and TelecommunicationsCN5 papers
- Huazhong University of Science and TechnologyCN5 papers
- University of Science and Technology of ChinaCN5 papers
- Beijing Institute of TechnologyCN4 papers
- Alibaba Group (China)CN3 papers
- Johns Hopkins UniversityUS3 papers
- New York Life Insurance Company (United States)US3 papers
71 papers
ChatSOP: An SOP-Guided MCTS Planning Framework for Controllable LLM Dialogue Agents
Zhigen Li, Jianxiang Peng, Yanmeng Wang +13
Dialogue agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and human-like responses, their **lack of…
Deep Learning Segmentation of Ascites on Abdominal CT Scans for Automatic Volume Quantification
Benjamin Hou, Sung-Won Lee, Jung-Min Lee +4
Purpose: To evaluate the performance of an automated deep learning method in detecting ascites and subsequently quantifying its volume in patients with liver cirrhosis and ovarian…
Retrieval-Augmented Audio Deepfake Detection
Zuheng Kang, Yayun He, Botao Zhao +4
With recent advances in speech synthesis including text-to-speech (TTS) and voice conversion (VC) systems enabling the generation of ultra-realistic audio deepfakes, there is growi…
Enhancing Low-Resource Relation Representations through Multi-View Decoupling
Chenghao Fan, Wei Wei, Xiaoye Qu +4
Recently, prompt-tuning with pre-trained language models (PLMs) has demonstrated the significantly enhancing ability of relation extraction (RE) tasks. However, in low-resource sce…
PMVC: Data Augmentation-Based Prosody Modeling for Expressive Voice Conversion
Yimin Deng, Huaizhen Tang, Xulong Zhang +3
Voice conversion as the style transfer task applied to speech, refers to converting one person's speech into a new speech that sounds like another person's. Up to now, there has be…
Exploring the Impact of Model Scaling on Parameter-Efficient Tuning
Yusheng Su, Chi-Min Chan, Jiali Cheng +9
Parameter-efficient tuning (PET) methods can effectively drive extremely large pre-trained language models (PLMs) by training only minimal parameters. Different PET methods utilize…