Search Results for author: Jiankun Zhang

Found 6 papers, 1 papers with code

The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)

1 code implementation23 Feb 2024 Shenglai Zeng, Jiankun Zhang, Pengfei He, Yue Xing, Yiding Liu, Han Xu, Jie Ren, Shuaiqiang Wang, Dawei Yin, Yi Chang, Jiliang Tang

In this work, we conduct extensive empirical studies with novel attack methods, which demonstrate the vulnerability of RAG systems on leaking the private retrieval database.

Language Modelling Retrieval

Copyright Protection in Generative AI: A Technical Perspective

no code implementations4 Feb 2024 Jie Ren, Han Xu, Pengfei He, Yingqian Cui, Shenglai Zeng, Jiankun Zhang, Hongzhi Wen, Jiayuan Ding, Hui Liu, Yi Chang, Jiliang Tang

We examine from two distinct viewpoints: the copyrights pertaining to the source data held by the data owners and those of the generative models maintained by the model builders.

Reconfigurable AI Modules Aided Channel Estimation and MIMO Detection

no code implementations29 Jan 2024 Xiangzhao Qin, Sha Hu, Jiankun Zhang, Jing Qian, Hao Wang

Deep learning (DL) based channel estimation (CE) and multiple input and multiple output detection (MIMODet), as two separate research topics, have provided convinced evidence to demonstrate the effectiveness and robustness of artificial intelligence (AI) for receiver design.

Super-Resolution

Soft MIMO Detection Using Marginal Posterior Probability Statistics

no code implementations17 Aug 2022 Jiankun Zhang, Hao Wang, Jing Qian, Zhenxing Gao

Soft demodulation of received symbols into bit log-likelihood ratios (LLRs) is at the very heart of multiple-input-multiple-output (MIMO) detection.

Online Learning Based NLOS Ranging Error Mitigation in 5G Positioning

no code implementations16 Aug 2022 Jiankun Zhang, Hao Wang

The fifth-generation (5G) wireless communication is useful for positioning due to its large bandwidth and low cost.

Residual-Aided End-to-End Learning of Communication System without Known Channel

no code implementations22 Feb 2021 Hao Jiang, Shuangkaisheng Bi, Linglong Dai, Hao Wang, Jiankun Zhang

However, the gradient vanishing and overfitting problems of GAN will result in the serious performance degradation of E2E learning of communication system.

Generative Adversarial Network

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