118 citations · 256 across the 31 of their papers we have counts for
8 papers · 1 filter
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…
Discovering Novel Biological Traits From Images Using Phylogeny-Guided Neural Networks
Mohannad Elhamod, Mridul Khurana, Harish Babu Manogaran +15
Discovering evolutionary traits that are heritable across species on the tree of life (also referred to as a phylogenetic tree) is of great interest to biologists to understand how…
Unified Out-Of-Distribution Detection: A Model-Specific Perspective
Reza Averly, Wei-Lun Chao
Out-of-distribution (OOD) detection aims to identify test examples that do not belong to the training distribution and are thus unlikely to be predicted reliably. Despite a plethor…
Making Batch Normalization Great in Federated Deep Learning
Jike Zhong, Hong-You Chen, Wei-Lun Chao
Batch Normalization (BN) is widely used in {centralized} deep learning to improve convergence and generalization. However, in {federated} learning (FL) with decentralized data, pri…
Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer Learning
Cheng-Hao Tu, Zheda Mai, Wei-Lun Chao
Intermediate features of a pre-trained model have been shown informative for making accurate predictions on downstream tasks, even if the model backbone is kept frozen. The key cha…
On the Importance and Applicability of Pre-Training for Federated Learning
Hong-You Chen, Cheng-Hao Tu, Ziwei Li +2
Pre-training is prevalent in nowadays deep learning to improve the learned model's performance. However, in the literature on federated learning (FL), neural networks are mostly in…
How to Train Your MAML to Excel in Few-Shot Classification
Han-Jia Ye, Wei-Lun Chao
Model-agnostic meta-learning (MAML) is arguably one of the most popular meta-learning algorithms nowadays. Nevertheless, its performance on few-shot classification is far behind ma…
Procrustean Training for Imbalanced Deep Learning
Han-Jia Ye, De-Chuan Zhan, Wei-Lun Chao
Neural networks trained with class-imbalanced data are known to perform poorly on minor classes of scarce training data. Several recent works attribute this to over-fitting to mino…