10 citations · 12 across the 6 of their papers we have counts for
7 papers
Interpretable intrinsic dimension estimation through componentwise calibration of distance and angle
Chih-Hsuan Huang, Chih-Wei Chen, Szu-Chi Chung
DANCo (Dimensionality from Angle and Norm Concentration) jointly calibrates nearest-neighbor distance and angular statistics and consistently reaches state-of-the-art accuracy on c…
The EEPAS Model Revisited: Statistical Formalism and a High-Performance, Reproducible Open-Source Framework
Szu-Chi Chung, Chien-Hong Cho, Strong Wen
While short-term models such as the Short-Term Earthquake Probability (STEP) and Epidemic-Type Aftershock Sequence (ETAS) are well established and supported by open-source software…
A Data-Centric Perspective on the Influence of Image Data Quality in Machine Learning Models
Pei-Han Chen, Szu-Chi Chung
In machine learning, research has traditionally focused on model development, with relatively less attention paid to training data. As model architectures have matured and marginal…
CRISP: A Framework for Cryo-EM Image Segmentation and Processing with Conditional Random Field
Szu-Chi Chung, Po-Cheng Chou
Differentiating signals from the background in micrographs is a critical initial step for cryogenic electron microscopy (cryo-EM), yet it remains laborious due to low signal-to-noi…
Cryo-forum: A framework for orientation recovery with uncertainty measure with the application in cryo-EM image analysis
Szu-Chi Chung
In single-particle cryo-electron microscopy (cryo-EM), the efficient determination of orientation parameters for 2D projection images poses a significant challenge yet is crucial f…
Cryo-RALib -- a modular library for accelerating alignment in cryo-EM
Szu-Chi Chung, Cheng-Yu Hung, Huei-Lun Siao +3
Thanks to automated cryo-EM and GPU-accelerated processing, single-particle cryo-EM has become a rapid structure determination method that permits capture of dynamical structures o…
Two-stage dimension reduction for noisy high-dimensional images and application to Cryogenic Electron Microscopy
Szu-Chi Chung, Shao-Hsuan Wang, Po-Yao Niu +3
Principal component analysis (PCA) is arguably the most widely used dimension-reduction method for vector-type data. When applied to a sample of images, PCA requires vectorization…