2 citations · 2 across the 11 of their papers we have counts for
14 papers
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…
SDG-L: A Semiparametric Deep Gaussian Process based Framework for Battery Capacity Prediction
Hanbing Liu, Yanru Wu, Yang Li +2
Lithium-ion batteries are becoming increasingly omnipresent in energy supply. However, the durability of energy storage using lithium-ion batteries is threatened by their dropping…
Reinforced Domain Selection for Continuous Domain Adaptation
Hanbing Liu, Huaze Tang, Yanru Wu +2
Continuous Domain Adaptation (CDA) effectively bridges significant domain shifts by progressively adapting from the source domain through intermediate domains to the target domain.…
Understanding Knowledge Transferability for Transfer Learning: A Survey
Haohua Wang, Jingge Wang, Zijie Zhao +9
Transfer learning has become an essential paradigm in artificial intelligence, enabling the transfer of knowledge from a source task to improve performance on a target task. This a…
Learning Optimal Prompt Ensemble for Multi-source Visual Prompt Transfer
Enming Zhang, Liwen Cao, Yanru Wu +2
Prompt tuning has emerged as a lightweight strategy for adapting foundation models to downstream tasks, particularly for resource-constrained systems. As pre-trained prompts become…
TMT: Cross-domain Semantic Segmentation with Region-adaptive Transferability Estimation
Enming Zhang, Zhengyu Li, Yanru Wu +5
Recent advances in Vision Transformers (ViTs) have significantly advanced semantic segmentation performance. However, their adaptation to new target domains remains challenged by d…
Exploiting Task Relationships in Continual Learning via Transferability-Aware Task Embeddings
Yanru Wu, Jianning Wang, Xiangyu Chen +4
Continual learning (CL) has been a critical topic in contemporary deep neural network applications, where higher levels of both forward and backward transfer are desirable for an e…
A High-Dimensional Statistical Method for Optimizing Transfer Quantities in Multi-Source Transfer Learning
Qingyue Zhang, Haohao Fu, Guanbo Huang +7
Multi-source transfer learning provides an effective solution to data scarcity in real-world supervised learning scenarios by leveraging multiple source tasks. In this field, exist…
pFedGPA: Diffusion-based Generative Parameter Aggregation for Personalized Federated Learning
Jiahao Lai, Jiaqi Li, Jian Xu +6
Federated Learning (FL) offers a decentralized approach to model training, where data remains local and only model parameters are shared between the clients and the central server.…
Balancing Speciality and Versatility: A Coarse to Fine Framework for Mitigating Catastrophic Forgetting in Large Language Models
Hengyuan Zhang, Yanru Wu, Dawei Li +4
Aligned Large Language Models (LLMs) showcase remarkable versatility, capable of handling diverse real-world tasks. Meanwhile, aligned LLMs are also expected to exhibit speciality,…
H-ensemble: An Information Theoretic Approach to Reliable Few-Shot Multi-Source-Free Transfer
Yanru Wu, Jianning Wang, Weida Wang +1
Multi-source transfer learning is an effective solution to data scarcity by utilizing multiple source tasks for the learning of the target task. However, access to source data and…