2 citations · 2 across the 11 of their papers we have counts for
4 papers · 1 filter
CORE-MTL: Rethinking Gradient Balancing via Causal Orthogonal Representations
Chengfeng Wu, Tao Zou, Yanru Wu +1
Multi-task learning (MTL) aims to construct a joint model for multiple tasks by sharing a common representation across domains. To achieve this goal, existing optimization-centric…
Heterogeneity-Aware Dataset Scheduling for Efficient Audio Large Language Model Training
Yanru Wu, Jianning Wang, Chongxin Gan +1
Training general-purpose Audio Large Language Models (ALLMs) across diverse datasets is essential for holistic audio understanding, yet it faces significant challenges due to datas…
Guided Prompt Evolution for Vision-Language Models Adaptation
Enming Zhang, Jiayang Li, Yanlong Wang +3
The adaptation of large-scale vision-language models (VLMs) to downstream tasks with limited labeled data remains a significant challenge. While parameter-efficient prompt learning…
Unified Optimization of Source Weights and Transfer Quantities in Multi-Source Transfer Learning: An Asymptotic Framework
Qingyue Zhang, Chang Chu, Haohao Fu +5
In multi-source transfer learning, a key challenge lies in how to appropriately differentiate and utilize heterogeneous source tasks. However, existing multi-source methods typical…