4 citations · 5 across the 4 of their papers we have counts for
4 papers
SimMIL: A Universal Weakly Supervised Pre-Training Framework for Multi-Instance Learning in Whole Slide Pathology Images
Yicheng Song, Tiancheng Lin, Die Peng +2
Various multi-instance learning (MIL) based approaches have been developed and successfully applied to whole-slide pathological images (WSI). Existing MIL methods emphasize the imp…
Learning Compositional Transferability of Time Series for Source-Free Domain Adaptation
Hankang Sun, Guiming Li, Su Yang +1
Domain adaptation is challenging for time series classification due to the highly dynamic nature. This study tackles the most difficult subtask when both target labels and source d…
DONOD: Efficient and Generalizable Instruction Fine-Tuning for LLMs via Model-Intrinsic Dataset Pruning
Jucheng Hu, Surong Yang, Lijun Wu +1
Ad-hoc instruction fine-tuning of large language models (LLMs) is widely adopted for domain-specific adaptation. While domain-specific supervised fine-tuning (SFT) is effective and…
Dance Generation with Style Embedding: Learning and Transferring Latent Representations of Dance Styles
Xinjian Zhang, Yi Xu, Su Yang +2
Choreography refers to creation of dance steps and motions for dances according to the latent knowledge in human mind, where the created dance motions are in general style-specific…