Search Results for author: Xin Mu

Found 8 papers, 1 papers with code

SecFormer: Towards Fast and Accurate Privacy-Preserving Inference for Large Language Models

no code implementations1 Jan 2024 Jinglong Luo, Yehong Zhang, JiaQi Zhang, Xin Mu, Hui Wang, Yue Yu, Zenglin Xu

However, the application of SMPC in Privacy-Preserving Inference (PPI) for large language models, particularly those based on the Transformer architecture, often leads to considerable slowdowns or declines in performance.

Knowledge Distillation Privacy Preserving

EncryIP: A Practical Encryption-Based Framework for Model Intellectual Property Protection

no code implementations19 Dec 2023 Xin Mu, Yu Wang, Zhengan Huang, Junzuo Lai, Yehong Zhang, Hui Wang, Yue Yu

In the rapidly growing digital economy, protecting intellectual property (IP) associated with digital products has become increasingly important.

Migrant Resettlement by Evolutionary Multi-objective Optimization

no code implementations13 Oct 2023 Dan-Xuan Liu, Yu-Ran Gu, Chao Qian, Xin Mu, Ke Tang

In this paper, we propose a new framework MR-EMO based on Evolutionary Multi-objective Optimization, which reformulates Migrant Resettlement as a bi-objective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a Multi-Objective Evolutionary Algorithm (MOEA) to solve the bi-objective problem.

Model Provenance via Model DNA

no code implementations4 Aug 2023 Xin Mu, Yu Wang, Yehong Zhang, JiaQi Zhang, Hui Wang, Yang Xiang, Yue Yu

Understanding the life cycle of the machine learning (ML) model is an intriguing area of research (e. g., understanding where the model comes from, how it is trained, and how it is used).

Representation Learning

A Generative Approach for Script Event Prediction via Contrastive Fine-tuning

1 code implementation7 Dec 2022 Fangqi Zhu, Jun Gao, Changlong Yu, Wei Wang, Chen Xu, Xin Mu, Min Yang, Ruifeng Xu

First, the pretrained language models adopted by current works ignore event-level knowledge, resulting in an inability to capture the correlations between events well.

Language Modelling

Data Provenance via Differential Auditing

no code implementations4 Sep 2022 Xin Mu, Ming Pang, Feida Zhu

In this paper, we introduce Data Provenance via Differential Auditing (DPDA), a practical framework for auditing data provenance with a different approach based on statistically significant differentials, i. e., after carefully designed transformation, perturbed input data from the target model's training set would result in much more drastic changes in the output than those from the model's non-training set.

Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees

no code implementations30 May 2016 Xin Mu, Kai Ming Ting, Zhi-Hua Zhou

This is the first time, as far as we know, that completely random trees are used as a single common core to solve all three sub problems: unsupervised learning, supervised learning and model update in data streams.

Classification General Classification

Cannot find the paper you are looking for? You can Submit a new open access paper.