Search Results for author: Di Zhuang

Found 11 papers, 4 papers with code

Epi-Curriculum: Episodic Curriculum Learning for Low-Resource Domain Adaptation in Neural Machine Translation

no code implementations6 Sep 2023 Keyu Chen, Di Zhuang, Mingchen Li, J. Morris Chang

Experiments on English-German and English-Romanian translation show that: (i) Epi-Curriculum improves both model's robustness and adaptability in seen and unseen domains; (ii) Our episodic training framework enhances the encoder and decoder's robustness to domain shift.

Domain Adaptation Machine Translation +2

MC-GEN:Multi-level Clustering for Private Synthetic Data Generation

1 code implementation28 May 2022 Mingchen Li, Di Zhuang, J. Morris Chang

MC-GEN applies multi-level clustering and differential private generative model to improve the utility of synthetic data.

BIG-bench Machine Learning Clustering +2

SuperCon: Supervised Contrastive Learning for Imbalanced Skin Lesion Classification

no code implementations11 Feb 2022 Keyu Chen, Di Zhuang, J. Morris Chang

The results show that our two-stage training strategy effectively addresses the class imbalance classification problem, and significantly improves existing works in terms of F1-score and AUC score, resulting in state-of-the-art performance.

Classification Contrastive Learning +2

Locally Differentially Private Distributed Deep Learning via Knowledge Distillation

1 code implementation7 Feb 2022 Di Zhuang, Mingchen Li, J. Morris Chang

As such, it motivates the researchers to conduct distributed deep learning, where the data user would like to build DL models using the data segregated across multiple different data owners.

Knowledge Distillation Privacy Preserving

Discriminative Adversarial Domain Generalization with Meta-learning based Cross-domain Validation

3 code implementations1 Nov 2020 Keyu Chen, Di Zhuang, J. Morris Chang

The generalization capability of machine learning models, which refers to generalizing the knowledge for an "unseen" domain via learning from one or multiple seen domain(s), is of great importance to develop and deploy machine learning applications in the real-world conditions.

BIG-bench Machine Learning Domain Generalization +1

Utility-aware Privacy-preserving Data Releasing

no code implementations9 May 2020 Di Zhuang, J. Morris Chang

Later, our approach leverages the learned knowledge to precisely perturb the data owners' data into privatized data that can be successfully utilized for certain intended purpose (learning to succeed), without jeopardizing certain predefined privacy (training to fail).

Human Activity Recognition Marketing +1

SAIA: Split Artificial Intelligence Architecture for Mobile Healthcare System

no code implementations25 Apr 2020 Di Zhuang, Nam Nguyen, Keyu Chen, J. Morris Chang

Hence, most of the mobile healthcare systems leverage the cloud computing infrastructure, where the data collected by the mobile and IoT devices would be transmitted to the cloud computing platforms for analysis.

Cloud Computing

CS-AF: A Cost-sensitive Multi-classifier Active Fusion Framework for Skin Lesion Classification

no code implementations25 Apr 2020 Di Zhuang, Keyu Chen, J. Morris Chang

Since the skin lesion datasets are usually limited and statistically biased, while designing an effective fusion approach, it is important to consider not only the performance of each classifier on the training/validation dataset, but also the relative discriminative power (e. g., confidence) of each classifier regarding an individual sample in the testing phase, which calls for an active fusion approach.

General Classification Lesion Classification +1

AutoGAN-based Dimension Reduction for Privacy Preservation

no code implementations27 Feb 2019 Hung Nguyen, Di Zhuang, Pei-Yuan Wu, Morris Chang

Protecting sensitive information against data exploiting attacks is an emerging research area in data mining.

Cryptography and Security

DynaMo: Dynamic Community Detection by Incrementally Maximizing Modularity

1 code implementation25 Sep 2017 Di Zhuang, J. Morris Chang, Mingchen Li

Community detection is of great importance for online social network analysis.

Social and Information Networks Cryptography and Security

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