no code implementations • 26 Apr 2024 • Shuchang Tao, Liuyi Yao, Hanxing Ding, Yuexiang Xie, Qi Cao, Fei Sun, Jinyang Gao, HuaWei Shen, Bolin Ding
Specifically, the order-preserving reward incentivizes the model to verbalize greater confidence for responses of higher quality to align the order of confidence and quality.
no code implementations • 31 Jan 2024 • Kaike Zhang, Qi Cao, Yunfan Wu, Fei Sun, HuaWei Shen, Xueqi Cheng
Traditional defense strategies predominantly depend on predefined assumptions or rules extracted from specific known attacks, limiting their generalizability to unknown attack types.
no code implementations • 22 Jan 2024 • Hexiang Tan, Fei Sun, Wanli Yang, Yuanzhuo Wang, Qi Cao, Xueqi Cheng
While auxiliary information has become a key to enhancing Large Language Models (LLMs), relatively little is known about how LLMs merge these contexts, specifically contexts generated by LLMs and those retrieved from external sources.
1 code implementation • 20 Jan 2024 • Dezhong Yao, Tongtong Liu, Qi Cao, Hai Jin
Federated Learning (FL) has emerged as a promising approach for preserving data privacy in recommendation systems by training models locally.
1 code implementation • 8 Dec 2023 • Zifeng An, Zijing Xu, Eric Fan, Qi Cao
This paper presents a novel approach to human image colorization by fine-tuning the InstructPix2Pix model, which integrates a language model (GPT-3) with a text-to-image model (Stable Diffusion).
1 code implementation • 30 Nov 2023 • Qi Cao, Takeshi Kojima, Yutaka Matsuo, Yusuke Iwasawa
While Large Language Models (LLMs) have achieved remarkable performance in many tasks, much about their inner workings remains unclear.
no code implementations • 5 Sep 2023 • Kaike Zhang, Qi Cao, Fei Sun, Yunfan Wu, Shuchang Tao, HuaWei Shen, Xueqi Cheng
With the rapid growth of information, recommender systems have become integral for providing personalized suggestions and overcoming information overload.
1 code implementation • 13 Jul 2023 • Hong Sun, Xue Li, Yinchuan Xu, Youkow Homma, Qi Cao, Min Wu, Jian Jiao, Denis Charles
This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM).
no code implementations • 25 May 2023 • Shuchang Tao, Qi Cao, HuaWei Shen, Yunfan Wu, Bingbing Xu, Xueqi Cheng
To address these limitations, we analyze the causalities in graph adversarial attacks and conclude that causal features are key to achieve graph adversarial robustness, owing to their determinedness for labels and invariance across attacks.
1 code implementation • 9 May 2023 • YuanHao Liu, Qi Cao, HuaWei Shen, Yunfan Wu, Shuchang Tao, Xueqi Cheng
In this paper, we propose a new criterion for popularity debiasing, i. e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion.
1 code implementation • 16 Feb 2023 • Shuchang Tao, HuaWei Shen, Qi Cao, Yunfan Wu, Liang Hou, Xueqi Cheng
In this paper, we propose and formulate graph adversarial immunization, i. e., vaccinating part of graph structure to improve certifiable robustness of graph against any admissible adversarial attack.
1 code implementation • 5 Feb 2023 • JunJie Huang, Qi Cao, Ruobing Xie, Shaoliang Zhang, Feng Xia, HuaWei Shen, Xueqi Cheng
To reduce the influence of data sparsity, Graph Contrastive Learning (GCL) is adopted in GNN-based CF methods for enhancing performance.
no code implementations • 20 Nov 2022 • Yige Yuan, Bingbing Xu, HuaWei Shen, Qi Cao, Keting Cen, Wen Zheng, Xueqi Cheng
Guided by the bound, we design a GCL framework named InfoAdv with enhanced generalization ability, which jointly optimizes the generalization metric and InfoMax to strike the right balance between pretext task fitting and the generalization ability on downstream tasks.
no code implementations • 16 Nov 2022 • Yang Li, Bingbing Xu, Qi Cao, Yige Yuan, HuaWei Shen
On account that previous studies either lacks variance analysis or only focus on a particular sampling paradigm, we firstly propose an unified node sampling variance analysis framework and analyze the core challenge "circular dependency" for deriving the minimum variance sampler, i. e., sampling probability depends on node embeddings while node embeddings can not be calculated until sampling is finished.
1 code implementation • 19 Oct 2022 • Kaike Zhang, Qi Cao, Gaolin Fang, Bingbing Xu, Hongjian Zou, HuaWei Shen, Xueqi Cheng
Unsupervised representation learning for dynamic graphs has attracted a lot of research attention in recent years.
1 code implementation • 3 Aug 2022 • Shuchang Tao, Qi Cao, HuaWei Shen, Yunfan Wu, Liang Hou, Fei Sun, Xueqi Cheng
In this paper, we first propose and define camouflage as distribution similarity between ego networks of injected nodes and normal nodes.
1 code implementation • NeurIPS 2023 • Liang Hou, Qi Cao, Yige Yuan, Songtao Zhao, Chongyang Ma, Siyuan Pan, Pengfei Wan, Zhongyuan Wang, HuaWei Shen, Xueqi Cheng
Training generative adversarial networks (GANs) with limited data is challenging because the discriminator is prone to overfitting.
no code implementations • 22 Mar 2022 • Zhaohui Wang, Qi Cao, HuaWei Shen, Bingbing Xu, Xueqi Cheng
The expressive power of message passing GNNs is upper-bounded by Weisfeiler-Lehman (WL) test.
1 code implementation • 30 Aug 2021 • Shuchang Tao, Qi Cao, HuaWei Shen, JunJie Huang, Yunfan Wu, Xueqi Cheng
In this paper, we focus on an extremely limited scenario of single node injection evasion attack, i. e., the attacker is only allowed to inject one single node during the test phase to hurt GNN's performance.
1 code implementation • 22 Aug 2021 • JunJie Huang, HuaWei Shen, Qi Cao, Shuchang Tao, Xueqi Cheng
Signed bipartite networks are different from classical signed networks, which contain two different node sets and signed links between two node sets.
2 code implementations • 21 Jul 2021 • Liang Hou, Qi Cao, HuaWei Shen, Siyuan Pan, Xiaoshuang Li, Xueqi Cheng
Specifically, the proposed auxiliary discriminative classifier becomes generator-aware by recognizing the class-labels of the real data and the generated data discriminatively.
Ranked #1 on Conditional Image Generation on Tiny ImageNet
1 code implementation • 12 Jul 2021 • Yunfan Wu, Qi Cao, HuaWei Shen, Shuchang Tao, Xueqi Cheng
INMO generates the inductive embeddings for users (items) by characterizing their interactions with some template items (template users), instead of employing an embedding lookup table.
2 code implementations • NeurIPS 2021 • Liang Hou, HuaWei Shen, Qi Cao, Xueqi Cheng
Recently, transformation-based self-supervised learning has been applied to generative adversarial networks (GANs) to mitigate catastrophic forgetting in the discriminator by introducing a stationary learning environment.
no code implementations • 5 Apr 2021 • Keke Zhao, Xing Zhao, Qi Cao, Linjian Mo
The framework can partition data into custom designed time buckets to capture the interactions among information aggregated in different time buckets.
no code implementations • 1 Jan 2021 • Xu Bingbing, HuaWei Shen, Qi Cao, YuanHao Liu, Keting Cen, Xueqi Cheng
For a target node, diverse sampling offers it diverse neighborhoods, i. e., rooted sub-graphs, and the representation of target node is finally obtained via aggregating the representation of diverse neighborhoods obtained using any GNN model.
no code implementations • 30 Dec 2020 • Mingqi Yuan, Qi Cao, Man-on Pun, Yi Chen
In this work, we develop practical user scheduling algorithms for downlink bursty traffic with emphasis on user fairness.
1 code implementation • 27 Jul 2020 • Bingbing Xu, Hua-Wei Shen, Qi Cao, Keting Cen, Xue-Qi Cheng
Graph convolutional networks gain remarkable success in semi-supervised learning on graph structured data.
2 code implementations • 19 Jul 2020 • Shuchang Tao, Hua-Wei Shen, Qi Cao, Liang Hou, Xue-Qi Cheng
Despite achieving strong performance in semi-supervised node classification task, graph neural networks (GNNs) are vulnerable to adversarial attacks, similar to other deep learning models.
no code implementations • 12 Jul 2020 • Di Xu, Zhen Li, Yanning Zhang, Qi Cao
This paper presents an illumination estimation method for virtual objects in real environment by learning.
1 code implementation • 21 Jun 2019 • Qi Cao, Hua-Wei Shen, Jinhua Gao, Bingzheng Wei, Xue-Qi Cheng
In this paper, we consider the problem of network-aware popularity prediction, leveraging both early adopters and social networks for popularity prediction.
no code implementations • 20 Jun 2019 • Keting Cen, Hua-Wei Shen, Jinhua Gao, Qi Cao, Bingbing Xu, Xue-Qi Cheng
In this paper, we address attributed network embedding from a novel perspective, i. e., learning node context representation for each node via modeling its attributed local subgraph.
1 code implementation • ICLR 2019 • Bingbing Xu, Hua-Wei Shen, Qi Cao, Yunqi Qiu, Xue-Qi Cheng
We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform.
Ranked #51 on Node Classification on Pubmed
1 code implementation • CIKM 2017 • Qi Cao, HuaWei Shen, Keting Cen, Wentao Ouyang, Xueqi Cheng
In this paper, we propose DeepHawkes to combat the defects of existing methods, leveraging end-to-end deep learning to make an analogy to interpretable factors of Hawkes process — a widely-used generative process to model information cascade.