1 citations · 1 across the 8 of their papers we have counts for
6 papers · 1 filter
CeRA: Breaking the Linear Ceiling of Low-Rank Adaptation with Non-linearity Retained at Inference
Hung-Hsuan Chen
Low-Rank Adaptation (LoRA) dominates parameter-efficient fine-tuning (PEFT). However, it faces a ``linear ceiling'': increasing the rank yields diminishing returns in expressive ca…
Dynamic DropConnect: Enhancing Neural Network Robustness through Adaptive Edge Dropping Strategies
Yuan-Chih Yang, Hung-Hsuan Chen
Dropout and DropConnect are well-known techniques that apply a consistent drop rate to randomly deactivate neurons or edges in a neural network layer during training. This paper in…
Flexible Bivariate Beta Mixture Model: A Probabilistic Approach for Clustering Complex Data Structures
Yung-Peng Hsu, Hung-Hsuan Chen
Clustering is essential in data analysis and machine learning, but traditional algorithms like -means and Gaussian Mixture Models (GMM) often fail with nonconvex clusters. To ad…
Understanding Gradient Boosting Classifier: Training, Prediction, and the Role of
Hung-Hsuan Chen
The Gradient Boosting Classifier (GBC) is a widely used machine learning algorithm for binary classification, which builds decision trees iteratively to minimize prediction errors.…
Multivariate Beta Mixture Model: Probabilistic Clustering With Flexible Cluster Shapes
Yung-Peng Hsu, Hung-Hsuan Chen
This paper introduces the multivariate beta mixture model (MBMM), a new probabilistic model for soft clustering. MBMM adapts to diverse cluster shapes because of the flexible proba…
Toward Efficient and Incremental Spectral Clustering via Parametric Spectral Clustering
Jo-Chun Chen, Hung-Hsuan Chen
Spectral clustering is a popular method for effectively clustering nonlinearly separable data. However, computational limitations, memory requirements, and the inability to perform…