no code implementations • 23 May 2024 • Dezhong Yao, Sanmu Li, Yutong Dai, Zhiqiang Xu, Shengshan Hu, Peilin Zhao, Lichao Sun
Federated continual learning (FCL) has received increasing attention due to its potential in handling real-world streaming data, characterized by evolving data distributions and varying client classes over time.
no code implementations • 30 Apr 2024 • Wen Yin, Jian Lou, Pan Zhou, Yulai Xie, Dan Feng, Yuhua Sun, Tailai Zhang, Lichao Sun
In the digital realm, we evaluate our approach using benchmark datasets for TIOD, achieving an Attack Success Rate (ASR) of up to 98. 21%.
1 code implementation • 24 Apr 2024 • Batu Guan, Yao Wan, Zhangqian Bi, Zheng Wang, Hongyu Zhang, Yulei Sui, Pan Zhou, Lichao Sun
As Large Language Models (LLMs) are increasingly used to automate code generation, it is often desired to know if the code is AI-generated and by which model, especially for purposes like protecting intellectual property (IP) in industry and preventing academic misconduct in education.
1 code implementation • 22 Apr 2024 • Yao Wan, Guanghua Wan, Shijie Zhang, Hongyu Zhang, Yulei Sui, Pan Zhou, Hai Jin, Lichao Sun
Subsequently, the membership classifier can be effectively employed to deduce the membership status of a given code sample based on the output of a target code completion model.
no code implementations • 26 Mar 2024 • Jiawen Shi, Zenghui Yuan, Yinuo Liu, Yue Huang, Pan Zhou, Lichao Sun, Neil Zhenqiang Gong
LLM-as-a-Judge is a novel solution that can assess textual information with large language models (LLMs).
1 code implementation • 20 Mar 2024 • Zhengqing Yuan, Ruoxi Chen, Zhaoxu Li, Haolong Jia, Lifang He, Chi Wang, Lichao Sun
Sora is the first large-scale generalist video generation model that garnered significant attention across society.
no code implementations • 15 Mar 2024 • Weixiang Sun, Yixin Liu, Zhiling Yan, Kaidi Xu, Lichao Sun
With the rapid growth of artificial intelligence (AI) in healthcare, there has been a significant increase in the generation and storage of sensitive medical data.
no code implementations • 11 Mar 2024 • WenTing Chen, Pengyu Wang, Hui Ren, Lichao Sun, Quanzheng Li, Yixuan Yuan, Xiang Li
To address these challenges, we propose a novel medical image synthesis model that leverages fine-grained image-text alignment and anatomy-pathology prompts to generate highly detailed and accurate synthetic medical images.
no code implementations • 11 Mar 2024 • Qing Xiao, Siyeop Yoon, Hui Ren, Matthew Tivnan, Lichao Sun, Quanzheng Li, Tianming Liu, Yu Zhang, Xiang Li
Alzheimer's Disease (AD) is a neurodegenerative condition characterized by diverse progression rates among individuals, with changes in cortical thickness (CTh) closely linked to its progression.
1 code implementation • 27 Feb 2024 • Yixin Liu, Kai Zhang, Yuan Li, Zhiling Yan, Chujie Gao, Ruoxi Chen, Zhengqing Yuan, Yue Huang, Hanchi Sun, Jianfeng Gao, Lifang He, Lichao Sun
Sora is a text-to-video generative AI model, released by OpenAI in February 2024.
1 code implementation • 19 Feb 2024 • Jinhao Duan, Renming Zhang, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, Kaidi Xu
As Large Language Models (LLMs) are integrated into critical real-world applications, their strategic and logical reasoning abilities are increasingly crucial.
no code implementations • 13 Feb 2024 • Yuqing Liu, Yu Wang, Lichao Sun, Philip S. Yu
We utilize user history as in-context user preferences to address the first challenge.
1 code implementation • 7 Feb 2024 • Dongping Chen, Ruoxi Chen, Shilin Zhang, Yinuo Liu, Yaochen Wang, Huichi Zhou, Qihui Zhang, Pan Zhou, Yao Wan, Lichao Sun
Multimodal Large Language Models (MLLMs) have gained significant attention recently, showing remarkable potential in artificial general intelligence.
2 code implementations • 31 Jan 2024 • Yuan Li, Yue Huang, Yuli Lin, Siyuan Wu, Yao Wan, Lichao Sun
Do large language models (LLMs) exhibit any forms of awareness similar to humans?
no code implementations • 30 Jan 2024 • Lulu Xue, Shengshan Hu, Ruizhi Zhao, Leo Yu Zhang, Shengqing Hu, Lichao Sun, Dezhong Yao
To mitigate the weaknesses of existing solutions, we propose a novel defense method, Dual Gradient Pruning (DGP), based on gradient pruning, which can improve communication efficiency while preserving the utility and privacy of CL.
1 code implementation • 19 Jan 2024 • Zhengliang Liu, Jason Holmes, Wenxiong Liao, Chenbin Liu, Lian Zhang, Hongying Feng, Peilong Wang, Muhammad Ali Elahi, Hongmin Cai, Lichao Sun, Quanzheng Li, Xiang Li, Tianming Liu, Jiajian Shen, Wei Liu
ROND is specifically designed to address this gap in the domain of radiation oncology, a field that offers many opportunities for NLP exploration.
2 code implementations • 11 Jan 2024 • Qihui Zhang, Chujie Gao, Dongping Chen, Yue Huang, Yixin Huang, Zhenyang Sun, Shilin Zhang, Weiye Li, Zhengyan Fu, Yao Wan, Lichao Sun
With the rapid development and widespread application of Large Language Models (LLMs), the use of Machine-Generated Text (MGT) has become increasingly common, bringing with it potential risks, especially in terms of quality and integrity in fields like news, education, and science.
1 code implementation • 10 Jan 2024 • Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric Xing, Furong Huang, Hao liu, Heng Ji, Hongyi Wang, huan zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
no code implementations • 9 Jan 2024 • Ke Zhang, Lichao Sun, Bolin Ding, Siu Ming Yiu, Carl Yang
Behemoth graphs are often fragmented and separately stored by multiple data owners as distributed subgraphs in many realistic applications.
2 code implementations • 28 Dec 2023 • Zhengqing Yuan, Zhaoxu Li, Weiran Huang, Yanfang Ye, Lichao Sun
In recent years, multimodal large language models (MLLMs) such as GPT-4V have demonstrated remarkable advancements, excelling in a variety of vision-language tasks.
no code implementations • 20 Dec 2023 • Zixiang Wei, Yiting Wang, Lichao Sun, Athanasios V. Vasilakos, Lin Wang
A class prediction block is then designed to classify the degradation information by calculating the structure similarity scores on the reflectance map and mean square error on the illumination map.
no code implementations • 12 Dec 2023 • Kaipeng Zheng, Weiran Huang, Lichao Sun
Our solution secures the 1st place in the MedFMC challenge.
no code implementations • 3 Dec 2023 • Eashan Adhikarla, Kai Zhang, Jun Yu, Lichao Sun, John Nicholson, Brian D. Davison
As a result, it raises concerns about the overall robustness of the machine learning techniques for computer vision applications that are deployed publicly for consumers.
2 code implementations • 30 Nov 2023 • Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Zhuoer Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang
We will provide public APIs for evaluating AlignBench with CritiqueLLM to facilitate the evaluation of LLMs' Chinese alignment.
no code implementations • 29 Nov 2023 • Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang
However, ViTs suffer from issues with explanation faithfulness, as their focal points are fragile to adversarial attacks and can be easily changed with even slight perturbations on the input image.
no code implementations • 23 Nov 2023 • Fei Kong, Jinhao Duan, Lichao Sun, Hao Cheng, Renjing Xu, HengTao Shen, Xiaofeng Zhu, Xiaoshuang Shi, Kaidi Xu
Though diffusion models excel in image generation, their step-by-step denoising leads to slow generation speeds.
1 code implementation • 22 Nov 2023 • Yixin Liu, Chenrui Fan, Yutong Dai, Xun Chen, Pan Zhou, Lichao Sun
To solve these challenges, we propose MetaCloak, which solves the bi-level poisoning problem with a meta-learning framework with an additional transformation sampling process to craft transferable and robust perturbation.
1 code implementation • 22 Nov 2023 • Yixin Liu, Kaidi Xu, Xun Chen, Lichao Sun
Observing that simply removing the adversarial noise on the training process of the defensive noise can improve the performance of robust unlearnable examples, we identify that solely the surrogate model's robustness contributes to the performance.
no code implementations • 15 Nov 2023 • Yuanwei Wu, Xiang Li, Yixin Liu, Pan Zhou, Lichao Sun
This finding indicates potential exploitable security risks in MLLMs; 2) Based on the acquired system prompts, we propose a novel MLLM jailbreaking attack method termed SASP (Self-Adversarial Attack via System Prompt).
no code implementations • 10 Nov 2023 • Zhengliang Liu, Hanqi Jiang, Tianyang Zhong, Zihao Wu, Chong Ma, Yiwei Li, Xiaowei Yu, Yutong Zhang, Yi Pan, Peng Shu, Yanjun Lyu, Lu Zhang, Junjie Yao, Peixin Dong, Chao Cao, Zhenxiang Xiao, Jiaqi Wang, Huan Zhao, Shaochen Xu, Yaonai Wei, Jingyuan Chen, Haixing Dai, Peilong Wang, Hao He, Zewei Wang, Xinyu Wang, Xu Zhang, Lin Zhao, Yiheng Liu, Kai Zhang, Liheng Yan, Lichao Sun, Jun Liu, Ning Qiang, Bao Ge, Xiaoyan Cai, Shijie Zhao, Xintao Hu, Yixuan Yuan, Gang Li, Shu Zhang, Xin Zhang, Xi Jiang, Tuo Zhang, Dinggang Shen, Quanzheng Li, Wei Liu, Xiang Li, Dajiang Zhu, Tianming Liu
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
1 code implementation • 29 Oct 2023 • Zhiling Yan, Kai Zhang, Rong Zhou, Lifang He, Xiang Li, Lichao Sun
In this paper, we critically evaluate the capabilities of the state-of-the-art multimodal large language model, i. e., GPT-4 with Vision (GPT-4V), on Visual Question Answering (VQA) task.
no code implementations • 18 Oct 2023 • Jiawei Liu, Cheng Yang, Zhiyuan Lu, Junze Chen, Yibo Li, Mengmei Zhang, Ting Bai, Yuan Fang, Lichao Sun, Philip S. Yu, Chuan Shi
Foundation models have emerged as critical components in a variety of artificial intelligence applications, and showcase significant success in natural language processing and several other domains.
1 code implementation • 10 Oct 2023 • Yuan Li, Yixuan Zhang, Lichao Sun
We propose a novel framework that equips collaborative generative agents with human-like reasoning abilities and specialized skills.
no code implementations • 8 Oct 2023 • Yue Huang, Lichao Sun
The rampant spread of fake news has adversely affected society, resulting in extensive research on curbing its spread.
no code implementations • NeurIPS 2023 • Sili Huang, Yanchao Sun, Jifeng Hu, Siyuan Guo, Hechang Chen, Yi Chang, Lichao Sun, Bo Yang
Our experimental results demonstrate that SGFD can generalize well on a wide range of test environments and significantly outperforms state-of-the-art methods in handling both task-irrelevant variations and task-relevant variations.
1 code implementation • 4 Oct 2023 • Yue Huang, Jiawen Shi, Yuan Li, Chenrui Fan, Siyuan Wu, Qihui Zhang, Yixin Liu, Pan Zhou, Yao Wan, Neil Zhenqiang Gong, Lichao Sun
However, in scenarios where LLMs serve as intelligent agents, as seen in applications like AutoGPT and MetaGPT, LLMs are expected to engage in intricate decision-making processes that involve deciding whether to employ a tool and selecting the most suitable tool(s) from a collection of available tools to fulfill user requests.
no code implementations • 18 Sep 2023 • Shaika Chowdhury, Sivaraman Rajaganapathy, Lichao Sun, James Cerhan, Nansu Zong
In this study, we investigated the potential of GPT-3 for the anti-cancer drug sensitivity prediction task using structured pharmacogenomics data across five tissue types and evaluated its performance with zero-shot prompting and fine-tuning paradigms.
1 code implementation • 16 Sep 2023 • Cheng Chen, Juzheng Miao, Dufan Wu, Zhiling Yan, Sekeun Kim, Jiang Hu, Aoxiao Zhong, Zhengliang Liu, Lichao Sun, Xiang Li, Tianming Liu, Pheng-Ann Heng, Quanzheng Li
The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks.
3 code implementations • 23 Aug 2023 • Lai Wei, Zihao Jiang, Weiran Huang, Lichao Sun
To achieve this, we first propose several metrics to access the quality of multimodal instruction data.
1 code implementation • ICCV 2023 • Qiufan Ji, Lin Wang, Cong Shi, Shengshan Hu, Yingying Chen, Lichao Sun
In this paper, we first establish a comprehensive, and rigorous point cloud adversarial robustness benchmark to evaluate adversarial robustness, which can provide a detailed understanding of the effects of the defense and attack methods.
1 code implementation • 25 Jul 2023 • Zhengliang Liu, Tianyang Zhong, Yiwei Li, Yutong Zhang, Yi Pan, Zihao Zhao, Peixin Dong, Chao Cao, Yuxiao Liu, Peng Shu, Yaonai Wei, Zihao Wu, Chong Ma, Jiaqi Wang, Sheng Wang, Mengyue Zhou, Zuowei Jiang, Chunlin Li, Jason Holmes, Shaochen Xu, Lu Zhang, Haixing Dai, Kai Zhang, Lin Zhao, Yuanhao Chen, Xu Liu, Peilong Wang, Pingkun Yan, Jun Liu, Bao Ge, Lichao Sun, Dajiang Zhu, Xiang Li, Wei Liu, Xiaoyan Cai, Xintao Hu, Xi Jiang, Shu Zhang, Xin Zhang, Tuo Zhang, Shijie Zhao, Quanzheng Li, Hongtu Zhu, Dinggang Shen, Tianming Liu
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
no code implementations • 12 Jul 2023 • Yihan Cao, Yanbin Kang, Chi Wang, Lichao Sun
Large language models (LLMs) are initially pretrained for broad capabilities and then finetuned with instruction-following datasets to improve their performance in interacting with humans.
1 code implementation • 3 Jul 2023 • Haixing Dai, Chong Ma, Zhiling Yan, Zhengliang Liu, Enze Shi, Yiwei Li, Peng Shu, Xiaozheng Wei, Lin Zhao, Zihao Wu, Fang Zeng, Dajiang Zhu, Wei Liu, Quanzheng Li, Lichao Sun, Shu Zhang Tianming Liu, Xiang Li
Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts.
no code implementations • 20 Jun 2023 • Yue Huang, Qihui Zhang, Philip S. Y, Lichao Sun
Through the implementation of TrustGPT, this research aims to enhance our understanding of the performance of conversation generation models and promote the development of language models that are more ethical and socially responsible.
no code implementations • 14 Jun 2023 • Zhengliang Liu, Aoxiao Zhong, Yiwei Li, Longtao Yang, Chao Ju, Zihao Wu, Chong Ma, Peng Shu, Cheng Chen, Sekeun Kim, Haixing Dai, Lin Zhao, Lichao Sun, Dajiang Zhu, Jun Liu, Wei Liu, Dinggang Shen, Xiang Li, Quanzheng Li, Tianming Liu
We introduce Radiology-GPT, a large language model for radiology.
no code implementations • 13 Jun 2023 • Siyuan Guo, Yanchao Sun, Jifeng Hu, Sili Huang, Hechang Chen, Haiyin Piao, Lichao Sun, Yi Chang
However, constrained by the limited quality of the offline dataset, its performance is often sub-optimal.
no code implementations • 8 Jun 2023 • Jifeng Hu, Yanchao Sun, Sili Huang, Siyuan Guo, Hechang Chen, Li Shen, Lichao Sun, Yi Chang, DaCheng Tao
Recent works have shown the potential of diffusion models in computer vision and natural language processing.
1 code implementation • 8 Jun 2023 • Shanshan Han, Baturalp Buyukates, Zijian Hu, Han Jin, Weizhao Jin, Lichao Sun, Xiaoyang Wang, Wenxuan Wu, Chulin Xie, Yuhang Yao, Kai Zhang, Qifan Zhang, Yuhui Zhang, Carlee Joe-Wong, Salman Avestimehr, Chaoyang He
This paper introduces FedSecurity, an end-to-end benchmark designed to simulate adversarial attacks and corresponding defense mechanisms in Federated Learning (FL).
no code implementations • 2 Jun 2023 • Liangqi Yuan, Ziran Wang, Lichao Sun, Philip S. Yu, Christopher G. Brinton
Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing communication overhead.
1 code implementation • 26 May 2023 • Kai Zhang, Jun Yu, Eashan Adhikarla, Rong Zhou, Zhiling Yan, Yixin Liu, Zhengliang Liu, Lifang He, Brian Davison, Xiang Li, Hui Ren, Sunyang Fu, James Zou, Wei Liu, Jing Huang, Chen Chen, Yuyin Zhou, Tianming Liu, Xun Chen, Yong Chen, Quanzheng Li, Hongfang Liu, Lichao Sun
Conventional task- and modality-specific artificial intelligence (AI) models are inflexible in real-world deployment and maintenance for biomedicine.
Ranked #1 on Text Summarization on MeQSum
1 code implementation • 25 May 2023 • Yingqian Cui, Jie Ren, Han Xu, Pengfei He, Hui Liu, Lichao Sun, Yue Xing, Jiliang Tang
By detecting the watermark from generated images, copyright infringement can be exposed with evidence.
1 code implementation • 12 May 2023 • Zhengqing Yuan, Yunhong He, Kun Wang, Yanfang Ye, Lichao Sun
However, a grand challenge of exploiting LLMs for multimodal learning is the size of pre-trained LLMs which are always with billions of parameters.
no code implementations • 28 Apr 2023 • Jiaqi Wang, Enze Shi, Sigang Yu, Zihao Wu, Chong Ma, Haixing Dai, Qiushi Yang, Yanqing Kang, Jinru Wu, Huawen Hu, Chenxi Yue, Haiyang Zhang, Yiheng Liu, Yi Pan, Zhengliang Liu, Lichao Sun, Xiang Li, Bao Ge, Xi Jiang, Dajiang Zhu, Yixuan Yuan, Dinggang Shen, Tianming Liu, Shu Zhang
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks.
1 code implementation • 20 Mar 2023 • Zhengliang Liu, Yue Huang, Xiaowei Yu, Lu Zhang, Zihao Wu, Chao Cao, Haixing Dai, Lin Zhao, Yiwei Li, Peng Shu, Fang Zeng, Lichao Sun, Wei Liu, Dinggang Shen, Quanzheng Li, Tianming Liu, Dajiang Zhu, Xiang Li
The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy.
1 code implementation • 8 Mar 2023 • Kai Zhang, Yutong Dai, Hongyi Wang, Eric Xing, Xun Chen, Lichao Sun
Federated learning is a promising paradigm that allows multiple clients to collaboratively train a model without sharing the local data.
1 code implementation • 7 Mar 2023 • Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S. Yu, Lichao Sun
The goal of AIGC is to make the content creation process more efficient and accessible, allowing for the production of high-quality content at a faster pace.
no code implementations • 5 Mar 2023 • Yixin Liu, Chenrui Fan, Pan Zhou, Lichao Sun
While the use of graph-structured data in various fields is becoming increasingly popular, it also raises concerns about the potential unauthorized exploitation of personal data for training commercial graph neural network (GNN) models, which can compromise privacy.
no code implementations • 5 Mar 2023 • Yixin Liu, Haohui Ye, Kai Zhang, Lichao Sun
The volume of open-source biomedical data has been essential to the development of various spheres of the healthcare community since more `free' data can provide individual researchers more chances to contribute.
no code implementations • 25 Feb 2023 • Haixing Dai, Zhengliang Liu, Wenxiong Liao, Xiaoke Huang, Yihan Cao, Zihao Wu, Lin Zhao, Shaochen Xu, Wei Liu, Ninghao Liu, Sheng Li, Dajiang Zhu, Hongmin Cai, Lichao Sun, Quanzheng Li, Dinggang Shen, Tianming Liu, Xiang Li
Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks.
no code implementations • 21 Feb 2023 • Jiawen Shi, Yixin Liu, Pan Zhou, Lichao Sun
Recently, ChatGPT has gained significant attention in research due to its ability to interact with humans effectively.
no code implementations • 18 Feb 2023 • Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, JianXin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun
This study provides a comprehensive review of recent research advancements, challenges, and opportunities for PFMs in text, image, graph, as well as other data modalities.
no code implementations • 2 Jan 2023 • Jiahao Zhu, Daizong Liu, Pan Zhou, Xing Di, Yu Cheng, Song Yang, Wenzheng Xu, Zichuan Xu, Yao Wan, Lichao Sun, Zeyu Xiong
All existing works first utilize a sparse sampling strategy to extract a fixed number of video frames and then conduct multi-modal interactions with query sentence for reasoning.
1 code implementation • 6 Dec 2022 • Yutong Dai, Zeyuan Chen, Junnan Li, Shelby Heinecke, Lichao Sun, ran Xu
We propose FedNH, a novel method that improves the local models' performance for both personalization and generalization by combining the uniformity and semantics of class prototypes.
no code implementations • 23 Nov 2022 • Lijie Hu, Yixin Liu, Ninghao Liu, Mengdi Huai, Lichao Sun, Di Wang
Results show that SEAT is more stable against different perturbations and randomness while also keeps the explainability of attention, which indicates it is a more faithful explanation.
no code implementations • 22 Nov 2022 • Shengshan Hu, Junwei Zhang, Wei Liu, Junhui Hou, Minghui Li, Leo Yu Zhang, Hai Jin, Lichao Sun
In addition, existing attack approaches towards point cloud classifiers cannot be applied to the completion models due to different output forms and attack purposes.
1 code implementation • 18 Oct 2022 • Jie Ren, Han Xu, Yuxuan Wan, Xingjun Ma, Lichao Sun, Jiliang Tang
The unlearnable strategies have been introduced to prevent third parties from training on the data without permission.
1 code implementation • 14 Oct 2022 • Jifeng Hu, Yanchao Sun, Hechang Chen, Sili Huang, Haiyin Piao, Yi Chang, Lichao Sun
Our main idea is to design the multi-action-branch reward estimation and policy-weighted reward aggregation for stabilized training.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 22 Aug 2022 • Qucheng Peng, Zhengming Ding, Lingjuan Lyu, Lichao Sun, Chen Chen
For the input-level, we design a new data augmentation technique as Phase MixUp, which highlights task-relevant objects in the interpolations, thus enhancing input-level regularization and class consistency for target models.
2 code implementations • 21 Jun 2022 • Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li, George H. Chen, Zhihao Jia, Philip S. Yu
To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights.
no code implementations • 18 Jun 2022 • Jiaxiang Tang, Jinbao Zhu, Songze Li, Lichao Sun
We consider a federated representation learning framework, where with the assistance of a central server, a group of $N$ distributed clients train collaboratively over their private data, for the representations (or embeddings) of a set of entities (e. g., users in a social network).
1 code implementation • 25 May 2022 • Barry Menglong Yao, Aditya Shah, Lichao Sun, Jin-Hee Cho, Lifu Huang
We propose end-to-end multimodal fact-checking and explanation generation, where the input is a claim and a large collection of web sources, including articles, images, videos, and tweets, and the goal is to assess the truthfulness of the claim by retrieving relevant evidence and predicting a truthfulness label (e. g., support, refute or not enough information), and to generate a statement to summarize and explain the reasoning and ruling process.
no code implementations • 8 May 2022 • Yuanxin Zhuang, Lingjuan Lyu, Chuan Shi, Carl Yang, Lichao Sun
Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications.
1 code implementation • 26 Apr 2022 • Kay Liu, Yingtong Dou, Xueying Ding, Xiyang Hu, Ruitong Zhang, Hao Peng, Lichao Sun, Philip S. Yu
PyGOD is an open-source Python library for detecting outliers in graph data.
1 code implementation • 17 Mar 2022 • Kai Zhang, Yu Wang, Hongyi Wang, Lifu Huang, Carl Yang, Xun Chen, Lichao Sun
Furthermore, we propose a Federated learning paradigm with privacy-preserving Relation embedding aggregation (FedR) to tackle the privacy issue in FedE.
no code implementations • 29 Nov 2021 • Dezhong Yao, Wanning Pan, Michael J O'Neill, Yutong Dai, Yao Wan, Hai Jin, Lichao Sun
To this end, this paper proposes FedHM, a novel heterogeneous federated model compression framework, distributing the heterogeneous low-rank models to clients and then aggregating them into a full-rank model.
no code implementations • 21 Nov 2021 • Jun Yu, Zhaoming Kong, Aditya Kendre, Hao Peng, Carl Yang, Lichao Sun, Alex Leow, Lifang He
This paper presents a novel graph-based kernel learning approach for connectome analysis.
no code implementations • Findings (ACL) 2022 • Sijia Wang, Mo Yu, Shiyu Chang, Lichao Sun, Lifu Huang
Event extraction is typically modeled as a multi-class classification problem where event types and argument roles are treated as atomic symbols.
no code implementations • 7 Oct 2021 • Ce Zhou, Qiben Yan, Yan Shi, Lichao Sun
By exploiting the weaknesses of the stereo matching in depth estimation algorithms and the lens flare effect in optical imaging, we propose DoubleStar, a long-range attack that injects fake obstacle depth by projecting pure light from two complementary light sources.
no code implementations • 29 Sep 2021 • Yutong Dai, Xingjun Ma, Lichao Sun
Federated learning (FL) is a privacy-aware collaborative learning paradigm that allows multiple parties to jointly train a machine learning model without sharing their private data.
1 code implementation • 13 Sep 2021 • Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Xuyun Zhang
However, existing MIAs ignore the source of a training member, i. e., the information of which client owns the training member, while it is essential to explore source privacy in FL beyond membership privacy of examples from all clients.
no code implementations • ICLR 2022 • Zhiyuan Zhang, Lingjuan Lyu, Weiqiang Wang, Lichao Sun, Xu sun
In this work, we observe an interesting phenomenon that the variations of parameters are always AWPs when tuning the trained clean model to inject backdoors.
1 code implementation • 26 Aug 2021 • Yu Wang, Zhiwei Liu, Ziwei Fan, Lichao Sun, Philip S. Yu
In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information.
1 code implementation • 31 Jul 2021 • Zhaoming Kong, Lichao Sun, Hao Peng, Liang Zhan, Yong Chen, Lifang He
In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis.
no code implementations • 7 Jul 2021 • Yanqiao Zhu, Hejie Cui, Lifang He, Lichao Sun, Carl Yang
Multimodal brain networks characterize complex connectivities among different brain regions from both structural and functional aspects and provide a new means for mental disease analysis.
no code implementations • 30 Jun 2021 • Dezhong Yao, Wanning Pan, Yutong Dai, Yao Wan, Xiaofeng Ding, Hai Jin, Zheng Xu, Lichao Sun
Federated learning enables multiple clients to collaboratively learn a global model by periodically aggregating the clients' models without transferring the local data.
1 code implementation • NeurIPS 2021 • Ke Zhang, Carl Yang, Xiaoxiao Li, Lichao Sun, Siu Ming Yiu
Graphs have been widely used in data mining and machine learning due to their unique representation of real-world objects and their interactions.
1 code implementation • 7 May 2021 • Gongxu Luo, JianXin Li, Jianlin Su, Hao Peng, Carl Yang, Lichao Sun, Philip S. Yu, Lifang He
Based on them, we design MinGE to directly calculate the ideal node embedding dimension for any graph.
no code implementations • 4 May 2021 • Sicong Che, Hao Peng, Lichao Sun, Yong Chen, Lifang He
This paper aims to provide a generic Federated Multi-View Learning (FedMV) framework for multi-view data leakage prevention, which is based on different types of local data availability and enables to accommodate two types of problems: Vertical Federated Multi-View Learning (V-FedMV) and Horizontal Federated Multi-View Learning (H-FedMV).
2 code implementations • 25 Apr 2021 • Yingtong Dou, Kai Shu, Congying Xia, Philip S. Yu, Lichao Sun
The majority of existing fake news detection algorithms focus on mining news content and/or the surrounding exogenous context for discovering deceptive signals; while the endogenous preference of a user when he/she decides to spread a piece of fake news or not is ignored.
Ranked #1 on Graph Classification on UPFD-GOS
no code implementations • 16 Apr 2021 • Yu Wang, Lifu Huang, Philip S. Yu, Lichao Sun
Membership inference attacks (MIAs) infer whether a specific data record is used for target model training.
1 code implementation • 14 Apr 2021 • Chaoyang He, Keshav Balasubramanian, Emir Ceyani, Carl Yang, Han Xie, Lichao Sun, Lifang He, Liangwei Yang, Philip S. Yu, Yu Rong, Peilin Zhao, Junzhou Huang, Murali Annavaram, Salman Avestimehr
FedGraphNN is built on a unified formulation of graph FL and contains a wide range of datasets from different domains, popular GNN models, and FL algorithms, with secure and efficient system support.
1 code implementation • NAACL 2021 • Xuanli He, Lingjuan Lyu, Qiongkai Xu, Lichao Sun
Finally, we investigate two defence strategies to protect the victim model and find that unless the performance of the victim model is sacrificed, both model ex-traction and adversarial transferability can effectively compromise the target models
2 code implementations • 14 Mar 2021 • Hongsheng Hu, Zoran Salcic, Lichao Sun, Gillian Dobbie, Philip S. Yu, Xuyun Zhang
In recent years, MIAs have been shown to be effective on various ML models, e. g., classification models and generative models.
1 code implementation • 6 Feb 2021 • Xiaohang Xu, Hao Peng, Lichao Sun, Md Zakirul Alam Bhuiyan, Lianzhong Liu, Lifang He
Depression is one of the most common mental illness problems, and the symptoms shown by patients are not consistent, making it difficult to diagnose in the process of clinical practice and pathological research.
no code implementations • 1 Jan 2021 • Xuanli He, Lingjuan Lyu, Lichao Sun, Xiaojun Chang, Jun Zhao
We then demonstrate how the extracted model can be exploited to develop effective attribute inference attack to expose sensitive information of the training data.
no code implementations • 7 Dec 2020 • Lingjuan Lyu, Han Yu, Xingjun Ma, Chen Chen, Lichao Sun, Jun Zhao, Qiang Yang, Philip S. Yu
Besides training powerful global models, it is of paramount importance to design FL systems that have privacy guarantees and are resistant to different types of adversaries.
no code implementations • COLING 2020 • Lichao Sun, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu, Lifang He
Our studies show that mixup is a domain-independent data augmentation technique to pre-trained language models, resulting in significant performance improvement for transformer-based models.
no code implementations • 28 Sep 2020 • Carl Yang, Haonan Wang, Ke Zhang, Lichao Sun
Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).
no code implementations • 11 Sep 2020 • Lichao Sun, Lingjuan Lyu
Conventional federated learning directly averages model weights, which is only possible for collaboration between models with homogeneous architectures.
2 code implementations • 2 Aug 2020 • Qian Li, Hao Peng, Jian-Xin Li, Congying Xia, Renyu Yang, Lichao Sun, Philip S. Yu, Lifang He
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning.
no code implementations • 31 Jul 2020 • Lichao Sun, Jianwei Qian, Xun Chen
In this paper, we proposed a novel design of local differential privacy mechanism for federated learning to address the abovementioned issues.
no code implementations • 29 Jun 2020 • Lichao Sun
Recently, advanced NLP models have seen a surge in the usage of various applications.
1 code implementation • 1 May 2020 • Carl Yang, Haonan Wang, Ke Zhang, Liang Chen, Lichao Sun
Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).
2 code implementations • 25 Apr 2020 • Chandra Thapa, M. A. P. Chamikara, Seyit Camtepe, Lichao Sun
SL provides better model privacy than FL due to the machine learning model architecture split between clients and the server.
no code implementations • 1 Mar 2020 • Lichao Sun, Yingbo Zhou, Philip S. Yu, Caiming Xiong
Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice.
no code implementations • 27 Feb 2020 • Lichao Sun, Kazuma Hashimoto, Wenpeng Yin, Akari Asai, Jia Li, Philip Yu, Caiming Xiong
There is an increasing amount of literature that claims the brittleness of deep neural networks in dealing with adversarial examples that are created maliciously.
no code implementations • 25 Sep 2019 • Lichao Sun, Yingbo Zhou, Jia Li, Richard Socher, Philip S. Yu, Caiming Xiong
Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice.
no code implementations • 5 Jun 2019 • Lichao Sun, Albert Chen, Philip S. Yu, Wei Chen
We incorporate self activation into influence propagation and propose the self-activation independent cascade (SAIC) model: nodes may be self activated besides being selected as seeds, and influence propagates from both selected seeds and self activated nodes.
Social and Information Networks
no code implementations • 5 Jun 2019 • Lichao Sun, Yingbo Zhou, Ji Wang, Jia Li, Richard Sochar, Philip S. Yu, Caiming Xiong
Privacy-preserving deep learning is crucial for deploying deep neural network based solutions, especially when the model works on data that contains sensitive information.
1 code implementation • 26 Dec 2018 • Lichao Sun, Yingtong Dou, Carl Yang, Ji Wang, Yixin Liu, Philip S. Yu, Lifang He, Bo Li
Therefore, this review is intended to provide an overall landscape of more than 100 papers on adversarial attack and defense strategies for graph data, and establish a unified formulation encompassing most graph adversarial learning models.
no code implementations • 13 Nov 2018 • Ji Wang, Weidong Bao, Lichao Sun, Xiaomin Zhu, Bokai Cao, Philip S. Yu
To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA.
no code implementations • 11 Sep 2018 • Lichao Sun, Lifang He, Zhipeng Huang, Bokai Cao, Congying Xia, Xiaokai Wei, Philip S. Yu
Meta-graph is currently the most powerful tool for similarity search on heterogeneous information networks, where a meta-graph is a composition of meta-paths that captures the complex structural information.
no code implementations • 10 Sep 2018 • Ji Wang, Bokai Cao, Philip S. Yu, Lichao Sun, Weidong Bao, Xiaomin Zhu
In this paper, we provide an overview of the current challenges and representative achievements about pushing deep learning on mobile devices from three aspects: training with mobile data, efficient inference on mobile devices, and applications of mobile deep learning.
1 code implementation • 12 Feb 2018 • Lichao Sun, Weiran Huang, Philip S. Yu, Wei Chen
In this paper, we study the Multi-Round Influence Maximization (MRIM) problem, where influence propagates in multiple rounds independently from possibly different seed sets, and the goal is to select seeds for each round to maximize the expected number of nodes that are activated in at least one round.
Social and Information Networks
no code implementations • 7 Nov 2017 • Lichao Sun, Xiaokai Wei, Jiawei Zhang, Lifang He, Philip S. Yu, Witawas Srisa-an
The results indicate that once we remove contaminants from the datasets, we can significantly improve both malware detection rate and detection accuracy
Cryptography and Security