12 citations · 36 across the 12 of their papers we have counts for
7 papers · 1 filter
Exploiting Meta-Learning-based Poisoning Attacks for Graph Link Prediction
Mingchen Li, Di Zhuang, Keyu Chen +2
Link prediction in graph data uses various algorithms and Graph Nerual Network (GNN) models to predict potential relationships between graph nodes. These techniques have found wide…
Synthetic Information towards Maximum Posterior Ratio for deep learning on Imbalanced Data
Hung Nguyen, Morris Chang
This study examines the impact of class-imbalanced data on deep learning models and proposes a technique for data balancing by generating synthetic data for the minority class. Unl…
Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation
Keyu Chen, Di Zhuang, Mingchen Li +1
Neural Machine Translation (NMT) models have become successful, but their performance remains poor when translating on new domains with a limited number of data. In this paper, we…
Locally Differentially Private Distributed Deep Learning via Knowledge Distillation
Di Zhuang, Mingchen Li, J. Morris Chang
Deep learning often requires a large amount of data. In real-world applications, e.g., healthcare applications, the data collected by a single organization (e.g., hospital) is ofte…
A compressive multi-kernel method for privacy-preserving machine learning
Thee Chanyaswad, J. Morris Chang, S. Y. Kung
As the analytic tools become more powerful, and more data are generated on a daily basis, the issue of data privacy arises. This leads to the study of the design of privacy-preserv…
Generating Black-Box Adversarial Examples in Sparse Domain
Hadi Zanddizari, Behnam Zeinali, J. Morris Chang
Applications of machine learning (ML) models and convolutional neural networks (CNNs) have been rapidly increased. Although state-of-the-art CNNs provide high accuracy in many appl…