6 papers
Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition
Zheda Mai, Ping Zhang, Cheng-Hao Tu +3
Parameter-efficient fine-tuning (PEFT) has attracted significant attention due to the growth of pre-trained model sizes and the need to fine-tune (FT) them for superior downstream…
Federated Inverse Probability Treatment Weighting for Individual Treatment Effect Estimation
Changchang Yin, Hong-You Chen, Wei-Lun Chao +1
Individual treatment effect (ITE) estimation is to evaluate the causal effects of treatment strategies on some important outcomes, which is a crucial problem in healthcare. Most ex…
FedNE: Surrogate-Assisted Federated Neighbor Embedding for Dimensionality Reduction
Ziwei Li, Xiaoqi Wang, Hong-You Chen +2
Federated learning (FL) has rapidly evolved as a promising paradigm that enables collaborative model training across distributed participants without exchanging their local data. D…
Fine-Tuning is Fine, if Calibrated
Zheda Mai, Arpita Chowdhury, Ping Zhang +8
Fine-tuning is arguably the most straightforward way to tailor a pre-trained model (e.g., a foundation model) to downstream applications, but it also comes with the risk of losing…
Reviving the Context: Camera Trap Species Classification as Link Prediction on Multimodal Knowledge Graphs
Vardaan Pahuja, Weidi Luo, Yu Gu +7
Camera traps are important tools in animal ecology for biodiversity monitoring and conservation. However, their practical application is limited by issues such as poor generalizati…
Jigsaw Game: Federated Clustering
Jinxuan Xu, Hong-You Chen, Wei-Lun Chao +1
Federated learning has recently garnered significant attention, especially within the domain of supervised learning. However, despite the abundance of unlabeled data on end-users,…