6 papers
Generative Cross-Entropy: A Strictly Proper Loss for Data-Efficient Classification
Qipeng Zhan, Zhuoping Zhou, Li Shen
Cross-entropy (CE) is the default training loss for supervised classification, but its sample efficiency is limited when labels are scarce. Existing remedies primarily act on the d…
Query-Aware Flow Diffusion for Graph-Based RAG with Retrieval Guarantees
Zhuoping Zhou, Davoud Ataee Tarzanagh, Sima Didari +7
Graph-based Retrieval-Augmented Generation (RAG) systems leverage interconnected knowledge structures to capture complex relationships that flat retrieval struggles with, enabling…
Bi-Lipschitz Autoencoder With Injectivity Guarantee
Qipeng Zhan, Zhuoping Zhou, Zexuan Wang +2
Autoencoders are widely used for dimensionality reduction, based on the assumption that high-dimensional data lies on low-dimensional manifolds. Regularized autoencoders aim to pre…
PCAE: Learning Ordered Representations in Latent Space for Intrinsic Dimension Estimation via Principal Component Autoencoder
Qipeng Zhan, Zhuoping Zhou, Zexuan Wang +1
Autoencoders have long been considered a nonlinear extension of Principal Component Analysis (PCA). Prior studies have demonstrated that linear autoencoders (LAEs) can recover the…
Fair CCA for Fair Representation Learning: An ADNI Study
Bojian Hou, Zhanliang Wang, Zhuoping Zhou +6
Canonical correlation analysis (CCA) is a technique for finding correlations between different data modalities and learning low-dimensional representations. As fairness becomes cru…
Multi-Scale Geometric Autoencoder
Qipeng Zhan, Zhuoping Zhou, Zexuan Wang +1
Autoencoders have emerged as powerful models for visualization and dimensionality reduction based on the fundamental assumption that high-dimensional data is generated from a low-d…