no code implementations • EMNLP 2020 • Shen Wang, Xiaokai Wei, Cicero Nogueira dos santos, Zhiguo Wang, Ramesh Nallapati, Andrew Arnold, Bing Xiang, Philip S. Yu
Existing knowledge graph embedding approaches concentrate on modeling symmetry/asymmetry, inversion, and composition typed relations but overlook the hierarchical nature of relations.
no code implementations • EMNLP 2021 • Tao Zhang, Congying Xia, Philip S. Yu, Zhiwei Liu, Shu Zhao
Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from high-resource domains to the low-resource target domain.
no code implementations • 24 Apr 2024 • Weizhi Zhang, Liangwei Yang, Zihe Song, Henry Peng Zou, Ke Xu, Yuanjie Zhu, Philip S. Yu
Graph contrastive learning aims to learn from high-order collaborative filtering signals with unsupervised augmentation on the user-item bipartite graph, which predominantly relies on the multi-task learning framework involving both the pair-wise recommendation loss and the contrastive loss.
1 code implementation • 24 Apr 2024 • Henry Peng Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip S. Yu, Cornelia Caragea
To address these limitations, we present ImplicitAVE, the first, publicly available multimodal dataset for implicit attribute value extraction.
no code implementations • 19 Apr 2024 • Hoang H. Nguyen, Chenwei Zhang, Ye Liu, Natalie Parde, Eugene Rohrbaugh, Philip S. Yu
Naively assuming English as a source language may hinder cross-lingual transfer for many languages by failing to consider the importance of language contact.
no code implementations • 12 Apr 2024 • Pu Li, Xiaoyan Yu, Hao Peng, Yantuan Xian, Linqin Wang, Li Sun, Jingyun Zhang, Philip S. Yu
In this paper, we approach social event detection from a new perspective based on Pre-trained Language Models (PLMs), and present RPLM_SED (Relational prompt-based Pre-trained Language Models for Social Event Detection).
no code implementations • 7 Apr 2024 • Libo Qin, Qiguang Chen, YuHang Zhou, Zhi Chen, Yinghui Li, Lizi Liao, Min Li, Wanxiang Che, Philip S. Yu
To this end, in this paper, we present a thorough review and provide a unified perspective to summarize the recent progress as well as emerging trends in multilingual large language models (MLLMs) literature.
no code implementations • 28 Mar 2024 • Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu
However, the gap between training objectives and the discrepancy between data distributions in pretraining and downstream tasks hinders the transfer of the pretrained knowledge.
no code implementations • 26 Mar 2024 • Shen Wang, Tianlong Xu, Hang Li, Chaoli Zhang, Joleen Liang, Jiliang Tang, Philip S. Yu, Qingsong Wen
The advent of Large Language Models (LLMs) has brought in a new era of possibilities in the realm of education.
no code implementations • 11 Mar 2024 • Fangxin Wang, Yuqing Liu, Kay Liu, Yibo Wang, Sourav Medya, Philip S. Yu
Therefore, identifying, quantifying, and utilizing uncertainty are essential to enhance the performance of the model for the downstream tasks as well as the reliability of the GNN predictions.
no code implementations • 7 Mar 2024 • Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, Ming Zhang
To tackle these issues, substantial efforts have been devoted to improving the performance of GNN models in practical real-world scenarios, as well as enhancing their reliability and robustness.
no code implementations • 26 Feb 2024 • Chaoguang Luo, Liuying Wen, Yong Qin, Liangwei Yang, Zhineng Hu, Philip S. Yu
In the DWHRec algorithm, the initial connections between users and listened tracks are represented by a weighted hypergraph.
no code implementations • 25 Feb 2024 • Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip S. Yu, Zhijiang Guo
To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses.
no code implementations • 24 Feb 2024 • Qian Ma, Hongliang Chi, Hengrui Zhang, Kay Liu, Zhiwei Zhang, Lu Cheng, Suhang Wang, Philip S. Yu, Yao Ma
The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community.
no code implementations • 18 Feb 2024 • Yinghui Li, Shang Qin, Jingheng Ye, Shirong Ma, Yangning Li, Libo Qin, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Philip S. Yu
To promote the CGEC field to better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC.
1 code implementation • 17 Feb 2024 • Xiangjue Dong, Yibo Wang, Philip S. Yu, James Caverlee
Our experiments demonstrate that all tested LLMs exhibit explicit and/or implicit gender bias, even when gender stereotypes are not present in the inputs.
1 code implementation • 16 Feb 2024 • Yinghui Li, Qingyu Zhou, Yuanzhen Luo, Shirong Ma, Yangning Li, Hai-Tao Zheng, Xuming Hu, Philip S. Yu
In this paper, we challenge the reasoning and understanding abilities of LLMs by proposing a FaLlacy Understanding Benchmark (FLUB) containing cunning questions that are easy for humans to understand but difficult for models to grasp.
no code implementations • 14 Feb 2024 • Chen Wang, Fangxin Wang, Ruocheng Guo, Yueqing Liang, Kay Liu, Philip S. Yu
Recognizing the critical role of confidence in aligning training objectives with evaluation metrics, we propose CPFT, a versatile framework that enhances recommendation confidence by integrating Conformal Prediction (CP)-based losses with CE loss during fine-tuning.
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 • 24 Jan 2024 • Wenjing Chang, Kay Liu, Kaize Ding, Philip S. Yu, Jianjun Yu
Firstly, by coupling node classification tasks, MITIGATE obtains the capability to detect out-of-distribution nodes without known anomalies.
no code implementations • 23 Jan 2024 • Li Sun, Zhenhao Huang, Hua Wu, Junda Ye, Hao Peng, Zhengtao Yu, Philip S. Yu
Graph Neural Networks (GNNs) have shown great power for learning and mining on graphs, and Graph Structure Learning (GSL) plays an important role in boosting GNNs with a refined graph.
no code implementations • 11 Jan 2024 • Liangwei Yang, Hengrui Zhang, Zihe Song, Jiawei Zhang, Weizhi Zhang, Jing Ma, Philip S. Yu
This paper answers a fundamental question in artificial neural network (ANN) design: We do not need to build ANNs layer-by-layer sequentially to guarantee the Directed Acyclic Graph (DAG) property.
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 • 2 Jan 2024 • Li Sun, Junda Ye, Jiawei Zhang, Yong Yang, Mingsheng Liu, Feiyang Wang, Philip S. Yu
To address the aforementioned issues, we propose a novel Contrastive model for Sequential Interaction Network learning on Co-Evolving RiEmannian spaces, CSINCERE.
no code implementations • 30 Dec 2023 • Yao Wan, Yang He, Zhangqian Bi, JianGuo Zhang, Hongyu Zhang, Yulei Sui, Guandong Xu, Hai Jin, Philip S. Yu
We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models.
1 code implementation • 29 Dec 2023 • Kay Liu, Hengrui Zhang, Ziqing Hu, Fangxin Wang, Philip S. Yu
One of the fundamental challenges confronting supervised graph outlier detection algorithms is the prevalent issue of class imbalance, where the scarcity of outlier instances compared to normal instances often results in suboptimal performance.
no code implementations • 26 Dec 2023 • Zi-Feng Mai, Chang-Dong Wang, Zhongjie Zeng, Ya Li, Jiaquan Chen, Philip S. Yu
To settle the above challenges, we propose a novel method HEKP4NBR, which transforms the knowledge graph (KG) into prompts, namely Knowledge Tree Prompt (KTP), to help PLM encode the OOV item IDs in the user's basket sequence.
1 code implementation • 19 Dec 2023 • Yuwei Cao, Hao Peng, Zhengtao Yu, Philip S. Yu
As a trending approach for social event detection, graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information, thus showing SOTA performance.
1 code implementation • 18 Dec 2023 • Wei-Yao Wang, Wen-Chih Peng, Wei Wang, Philip S. Yu
Agent forecasting systems have been explored to investigate agent patterns and improve decision-making in various domains, e. g., pedestrian predictions and marketing bidding.
1 code implementation • 18 Dec 2023 • Kun Peng, Lei Jiang, Hao Peng, Rui Liu, Zhengtao Yu, Jiaqian Ren, Zhifeng Hao, Philip S. Yu
Aspect Sentiment Triplet Extraction (ASTE) is an emerging task to extract a given sentence's triplets, which consist of aspects, opinions, and sentiments.
no code implementations • 18 Dec 2023 • Yu Wang, Zhiwei Liu, JianGuo Zhang, Weiran Yao, Shelby Heinecke, Philip S. Yu
With our principle, we managed to outperform GPT-Turbo-3. 5 on three datasets using 7b models e. g., Vicuna-7b and Openchat-7b on NDCG@10.
no code implementations • 17 Dec 2023 • Wenting Zhao, Ye Liu, Yao Wan, Yibo Wang, Qingyang Wu, Zhongfen Deng, Jiangshu Du, Shuaiqi Liu, Yunlong Xu, Philip S. Yu
Task-Oriented Parsing (TOP) enables conversational assistants to interpret user commands expressed in natural language, transforming them into structured outputs that combine elements of both natural language and intent/slot tags.
no code implementations • 13 Dec 2023 • Aiwei Liu, Leyi Pan, Yijian Lu, Jingjing Li, Xuming Hu, Xi Zhang, Lijie Wen, Irwin King, Hui Xiong, Philip S. Yu
Text watermarking algorithms play a crucial role in the copyright protection of textual content, yet their capabilities and application scenarios have been limited historically.
no code implementations • 26 Nov 2023 • Jinqi Lai, Wensheng Gan, Jiayang Wu, Zhenlian Qi, Philip S. Yu
With the rapid emergence and growing popularity of large models, it is evident that AI will drive transformation in the traditional judicial industry.
no code implementations • 22 Nov 2023 • Jiayang Wu, Wensheng Gan, Zefeng Chen, Shicheng Wan, Philip S. Yu
By addressing these aspects, this paper aims to facilitate a deeper understanding of multimodal models and their potential in various domains.
no code implementations • 21 Nov 2023 • Ke Xu, Yuanjie Zhu, Weizhi Zhang, Philip S. Yu
This inspired us to address the computational limitations of GCN-based models by designing a simple and efficient NODE-based model that can skip some GCN layers to reach the final state, thus avoiding the need to create many layers.
no code implementations • 20 Nov 2023 • Xiaolong Liu, Liangwei Yang, Chen Wang, Mingdai Yang, Zhiwei Liu, Philip S. Yu
Participant recommendation, a fundamental problem emerging together with GB, aims to find the participants for a launched group buying process with an initiator and a target item to increase the GB success rate.
1 code implementation • 16 Nov 2023 • Xiaolong Liu, Liangwei Yang, Zhiwei Liu, Xiaohan Li, Mingdai Yang, Chen Wang, Philip S. Yu
The users' group participation on social platforms reveals their interests and can be utilized as side information to mitigate the data sparsity and cold-start problem in recommender systems.
no code implementations • 14 Nov 2023 • Yibo Wang, Xiangjue Dong, James Caverlee, Philip S. Yu
DALA considers distribution shifts of adversarial examples to improve the attack's effectiveness under detection methods.
no code implementations • 13 Nov 2023 • Fanlong Zeng, Wensheng Gan, Yongheng Wang, Ning Liu, Philip S. Yu
Understanding and assessing this intelligence is a complex task.
no code implementations • 10 Nov 2023 • Wensheng Gan, Shicheng Wan, Philip S. Yu
MaaS is a new deployment and service paradigm for different AI-based models.
1 code implementation • 8 Nov 2023 • Xusheng Zhao, Hao Peng, Qiong Dai, Xu Bai, Huailiang Peng, Yanbing Liu, Qinglang Guo, Philip S. Yu
Aspect-based sentiment analysis (ABSA) is dedicated to forecasting the sentiment polarity of aspect terms within sentences.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +1
no code implementations • 7 Nov 2023 • Zhongfen Deng, Seunghyun Yoon, Trung Bui, Franck Dernoncourt, Quan Hung Tran, Shuaiqi Liu, Wenting Zhao, Tao Zhang, Yibo Wang, Philip S. Yu
Then we merge the sentences selected for a specific aspect as the input for the summarizer to produce the aspect-based summary.
1 code implementation • 7 Nov 2023 • Zhongfen Deng, Hao Peng, Tao Zhang, Shuaiqi Liu, Wenting Zhao, Yibo Wang, Philip S. Yu
Furthermore, the copy mechanism in value generator and the value attention module in value classifier help our model address the data discrepancy issue by only focusing on the relevant part of input text and ignoring other information which causes the discrepancy issue such as sentence structure in the text.
no code implementations • 2 Nov 2023 • Hao Niu, Yun Xiong, Xiaosu Wang, Philip S. Yu
Furthermore, we propose a dual-level graph attention network as a global encoder by fully employing dependency tag information to capture long-distance information effectively.
no code implementations • 1 Nov 2023 • Xiangjue Dong, Yibo Wang, Philip S. Yu, James Caverlee
Large Language Models (LLMs) can generate biased and toxic responses.
no code implementations • 1 Nov 2023 • Jiangnan Xia, Yu Yang, Senzhang Wang, Hongzhi Yin, Jiannong Cao, Philip S. Yu
To this end, we investigate a novel problem of robust POI recommendation by considering the uncertainty factors of the user check-ins, and proposes a Bayes-enhanced Multi-view Attention Network.
no code implementations • 31 Oct 2023 • Wenting Zhao, Ye Liu, Tong Niu, Yao Wan, Philip S. Yu, Shafiq Joty, Yingbo Zhou, Semih Yavuz
Moreover, a significant gap in the current landscape is the absence of a realistic benchmark for evaluating the effectiveness of grounding LLMs on heterogeneous knowledge sources (e. g., knowledge base and text).
1 code implementation • 30 Oct 2023 • Jiaqian Ren, Hao Peng, Lei Jiang, Zhiwei Liu, Jia Wu, Zhengtao Yu, Philip S. Yu
While in our observation, compared to the rarity of classes, the calibrated uncertainty estimated from well-trained evidential deep learning networks better reflects model performance.
1 code implementation • 25 Oct 2023 • Xuming Hu, Junzhe Chen, Aiwei Liu, Shiao Meng, Lijie Wen, Philip S. Yu
Additionally, our method is orthogonal to previous multimodal fusions, and using it on prior SOTA fusions further improves 5. 47% F1.
1 code implementation • 23 Oct 2023 • Hoang H. Nguyen, Ye Liu, Chenwei Zhang, Tao Zhang, Philip S. Yu
While Chain-of-Thought prompting is popular in reasoning tasks, its application to Large Language Models (LLMs) in Natural Language Understanding (NLU) is under-explored.
1 code implementation • 20 Oct 2023 • Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu
On the other hand, pretraining and finetuning on the same dataset leads to a high risk of overfitting.
1 code implementation • 20 Oct 2023 • Xiaolong Liu, Liangwei Yang, Zhiwei Liu, Mingdai Yang, Chen Wang, Hao Peng, Philip S. Yu
Collectively, our contributions signify a substantial stride towards augmenting the panorama of recommendation diversity within the realm of KG-informed RecSys paradigms.
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.
no code implementations • 13 Oct 2023 • Chen Wang, Liangwei Yang, Zhiwei Liu, Xiaolong Liu, Mingdai Yang, Yueqing Liang, Philip S. Yu
However, PLMs often overlook the vital collaborative filtering signals, leading to challenges in merging collaborative and semantic representation spaces and fine-tuning semantic representations for better alignment with warm-start conditions.
1 code implementation • 11 Oct 2023 • Zhongfen Deng, Wei-Te Chen, Lei Chen, Philip S. Yu
In this paper, we reformulate this task as a multi-label classification task that can be applied for real-world scenario in which only annotation of attribute values is available to train models (i. e., annotation of positional information of attribute values is not available).
no code implementations • 8 Oct 2023 • Xuming Hu, Junzhe Chen, Xiaochuan Li, Yufei Guo, Lijie Wen, Philip S. Yu, Zhijiang Guo
Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks.
no code implementations • 30 Sep 2023 • Haishuai Wang, Yang Gao, Xin Zheng, Peng Zhang, Hongyang Chen, Jiajun Bu, Philip S. Yu
In this paper, we integrate GPT-4 into GNAS and propose a new GPT-4 based Graph Neural Architecture Search method (GPT4GNAS for short).
1 code implementation • 28 Sep 2023 • Chunkai Zhang, Maohua Lyu, Huaijin Hao, Wensheng Gan, Philip S. Yu
For artificial intelligence, high-utility sequential rule mining (HUSRM) is a knowledge discovery method that can reveal the associations between events in the sequences.
1 code implementation • 24 Sep 2023 • Zheng Wang, Hongming Ding, Li Pan, Jianhua Li, Zhiguo Gong, Philip S. Yu
Graph-based semi-supervised learning (GSSL) has long been a hot research topic.
1 code implementation • 20 Sep 2023 • Yibo Wang, Wenting Zhao, Yao Wan, Zhongfen Deng, Philip S. Yu
In this paper, we propose to incorporate the label dependencies among entity types into a multi-task learning framework for better MRC-based NER.
no code implementations • 20 Sep 2023 • Wenting Zhao, Ye Liu, Yao Wan, Yibo Wang, Zhongfen Deng, Philip S. Yu
Furthermore, TAG-QA outperforms the end-to-end model T5 by 16% and 12% on BLEU-4 and PARENT F-score, respectively.
no code implementations • 7 Sep 2023 • Jiangshu Du, Congying Xia, Wenpeng Yin, TingTing Liang, Philip S. Yu
In intent detection tasks, leveraging meaningful semantic information from intent labels can be particularly beneficial for few-shot scenarios.
1 code implementation • 5 Sep 2023 • Guangjie Zeng, Hao Peng, Angsheng Li, Zhiwei Liu, Chunyang Liu, Philip S. Yu, Lifang He
In this work, we propose a novel unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED.
1 code implementation • 31 Aug 2023 • Yi Zhang, Yuying Zhao, Zhaoqing Li, Xueqi Cheng, Yu Wang, Olivera Kotevska, Philip S. Yu, Tyler Derr
Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain.
1 code implementation • 23 Aug 2023 • Junling Liu, Chao Liu, Peilin Zhou, Qichen Ye, Dading Chong, Kang Zhou, Yueqi Xie, Yuwei Cao, Shoujin Wang, Chenyu You, Philip S. Yu
The benchmark results indicate that LLMs displayed only moderate proficiency in accuracy-based tasks such as sequential and direct recommendation.
1 code implementation • 18 Aug 2023 • Liangwei Yang, Zhiwei Liu, Chen Wang, Mingdai Yang, Xiaolong Liu, Jing Ma, Philip S. Yu
To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph.
no code implementations • 16 Aug 2023 • Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu
With the proliferation of social media, a growing number of users search for and join group activities in their daily life.
1 code implementation • 9 Aug 2023 • Hoang H. Nguyen, Chenwei Zhang, Ye Liu, Philip S. Yu
Recent advanced methods in Natural Language Understanding for Task-oriented Dialogue (TOD) Systems (e. g., intent detection and slot filling) require a large amount of annotated data to achieve competitive performance.
2 code implementations • 30 Jul 2023 • Aiwei Liu, Leyi Pan, Xuming Hu, Shu'ang Li, Lijie Wen, Irwin King, Philip S. Yu
Experiments demonstrate that our algorithm attains high detection accuracy and computational efficiency through neural networks with a minimized number of parameters.
no code implementations • 29 Jul 2023 • Yibo Wang, Yanbing Xue, Bo Liu, Musen Wen, Wenting Zhao, Stephen Guo, Philip S. Yu
Position bias, the phenomenon whereby users tend to focus on higher-ranked items of the search result list regardless of the actual relevance to queries, is prevailing in many ranking systems.
1 code implementation • 10 Jul 2023 • Hoang H. Nguyen, Chenwei Zhang, Tao Zhang, Eugene Rohrbaugh, Philip S. Yu
Particularly, we propose unsupervised alignment objectives to capture (1) local one-to-one alignment between the two different modalities, (2) alignment via multi-modality contexts to leverage information from additional modalities, and (3) alignment via multilingual contexts where additional bilingual dictionaries are incorporated.
1 code implementation • 6 Jul 2023 • Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications.
1 code implementation • 28 Jun 2023 • Xi Wu, Liangwei Yang, Jibing Gong, Chao Zhou, Tianyu Lin, Xiaolong Liu, Philip S. Yu
To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models.
1 code implementation • 26 Jun 2023 • Yuwei Cao, Liangwei Yang, Chen Wang, Zhiwei Liu, Hao Peng, Chenyu You, Philip S. Yu
We explore the role of the fine-grained item attributes in bridging the gaps between the existing and the SCS items and pre-train a knowledgeable item-attribute graph for SCS item recommendation.
no code implementations • 25 Jun 2023 • Huiqiang Chen, Tianqing Zhu, Tao Zhang, Wanlei Zhou, Philip S. Yu
Federated learning (FL) has been a hot topic in recent years.
no code implementations • 21 Jun 2023 • Ziwei Fan, Zhiwei Liu, Hao Peng, Philip S. Yu
We also establish a correlation between the ranks of sequence and item embeddings and the rank of the user-item preference prediction matrix, which can affect recommendation diversity.
1 code implementation • 8 Jun 2023 • Xuan Lin, Lichang Dai, Yafang Zhou, Zu-Guo Yu, Wen Zhang, Jian-Yu Shi, Dong-Sheng Cao, Li Zeng, Haowen Chen, Bosheng Song, Philip S. Yu, Xiangxiang Zeng
Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs).
no code implementations • 6 Jun 2023 • Heng Xu, Tianqing Zhu, Lefeng Zhang, Wanlei Zhou, Philip S. Yu
Machine learning has attracted widespread attention and evolved into an enabling technology for a wide range of highly successful applications, such as intelligent computer vision, speech recognition, medical diagnosis, and more.
1 code implementation • 3 Jun 2023 • Mengzhu Sun, Xi Zhang, Jianqiang Ma, Sihong Xie, Yazheng Liu, Philip S. Yu
Rumor spreaders are increasingly utilizing multimedia content to attract the attention and trust of news consumers.
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.
no code implementations • 26 May 2023 • Xuming Hu, Aiwei Liu, Zeqi Tan, Xin Zhang, Chenwei Zhang, Irwin King, Philip S. Yu
These techniques neither preserve the semantic consistency of the original sentences when rule-based augmentations are adopted, nor preserve the syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations.
no code implementations • 25 May 2023 • Xuming Hu, Zhijiang Guo, Zhiyang Teng, Irwin King, Philip S. Yu
Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair.
no code implementations • 25 May 2023 • Xuming Hu, Junzhe Chen, Zhijiang Guo, Philip S. Yu
Evidence plays a crucial role in automated fact-checking.
1 code implementation • 22 May 2023 • Shuang Li, Xuming Hu, Aiwei Liu, Yawen Yang, Fukun Ma, Philip S. Yu, Lijie Wen
In this paper, we propose a novel Soft prompt learning framework with the Multilingual Verbalizer (SoftMV) for XNLI.
Cross-Lingual Natural Language Inference Cross-Lingual Transfer
no code implementations • 21 May 2023 • Guangsi Shi, Daokun Zhang, Ming Jin, Shirui Pan, Philip S. Yu
To better comprehend the complex physical laws, this paper proposes a novel learning based simulation model- Graph Networks with Spatial-Temporal neural Ordinary Equations (GNSTODE)- that characterizes the varying spatial and temporal dependencies in particle systems using a united end-to-end framework.
no code implementations • 12 May 2023 • Yawen Yang, Xuming Hu, Fukun Ma, Shu'ang Li, Aiwei Liu, Lijie Wen, Philip S. Yu
Existing works for nested NER ignore the recognition order and boundary position relation of nested entities.
no code implementations • 12 May 2023 • Ziwei Fan, Zhiwei Liu, Shelby Heinecke, JianGuo Zhang, Huan Wang, Caiming Xiong, Philip S. Yu
This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task, which pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs.
no code implementations • 5 May 2023 • Li Sun, Feiyang Wang, Junda Ye, Hao Peng, Philip S. Yu
On the other hand, contrastive learning boosts the deep graph clustering but usually struggles in either graph augmentation or hard sample mining.
1 code implementation • 2 May 2023 • Xuming Hu, Zhaochen Hong, Chenwei Zhang, Irwin King, Philip S. Yu
Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role.
1 code implementation • 2 May 2023 • Xuming Hu, Zhaochen Hong, Zhijiang Guo, Lijie Wen, Philip S. Yu
In light of this, we propose a fact verification model named ReRead to retrieve evidence and verify claim that: (1) Train the evidence retriever to obtain interpretable evidence (i. e., faithfulness and plausibility criteria); (2) Train the claim verifier to revisit the evidence retrieved by the optimized evidence retriever to improve the accuracy.
1 code implementation • 24 Apr 2023 • Xianghua Zeng, Hao Peng, Angsheng Li, Chunyang Liu, Lifang He, Philip S. Yu
State abstraction optimizes decision-making by ignoring irrelevant environmental information in reinforcement learning with rich observations.
no code implementations • 22 Apr 2023 • Yu Wang, Zhiwei Liu, Liangwei Yang, Philip S. Yu
Generative models have attracted significant interest due to their ability to handle uncertainty by learning the inherent data distributions.
1 code implementation • 6 Apr 2023 • Ziwei Fan, Ke Xu, Zhang Dong, Hao Peng, Jiawei Zhang, Philip S. Yu
Moreover, we show that the inclusion of user-user and item-item correlations can improve recommendations for users with both abundant and insufficient interactions.
2 code implementations • 17 Mar 2023 • Dongcheng Zou, Hao Peng, Xiang Huang, Renyu Yang, JianXin Li, Jia Wu, Chunyang Liu, Philip S. Yu
Graph Neural Networks (GNNs) are de facto solutions to structural data learning.
1 code implementation • 12 Mar 2023 • Aiwei Liu, Xuming Hu, Lijie Wen, Philip S. Yu
This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability.
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 • 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 • 13 Feb 2023 • Song Wu, Yazhou Ren, Aodi Yang, Xinyue Chen, Xiaorong Pu, Jing He, Liqiang Nie, Philip S. Yu
In this survey, we investigate the main contributions of deep learning applications using medical images in fighting against COVID-19 from the aspects of image classification, lesion localization, and severity quantification, and review different deep learning architectures and some image preprocessing techniques for achieving a preciser diagnosis.
2 code implementations • 9 Feb 2023 • Minqi Jiang, Chaochuan Hou, Ao Zheng, Xiyang Hu, Songqiao Han, Hailiang Huang, Xiangnan He, Philip S. Yu, Yue Zhao
Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news.
1 code implementation • 7 Feb 2023 • Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Jiawei Zhang, Yangyong Zhu, Philip S. Yu
Since group activities have become very common in daily life, there is an urgent demand for generating recommendations for a group of users, referred to as group recommendation task.
no code implementations • 31 Jan 2023 • Hengrui Zhang, Shen Wang, Vassilis N. Ioannidis, Soji Adeshina, Jiani Zhang, Xiao Qin, Christos Faloutsos, Da Zheng, George Karypis, Philip S. Yu
Graph Neural Networks (GNNs) are currently dominating in modeling graph-structure data, while their high reliance on graph structure for inference significantly impedes them from widespread applications.
no code implementations • 22 Jan 2023 • Lilin Zhang, Ning Yang, Yanchao Sun, Philip S. Yu
Second, the existing AT methods often achieve adversarial robustness at the expense of standard generalizability (i. e., the accuracy on natural examples) because they make a tradeoff between them.
no code implementations • 10 Jan 2023 • Xiaohan Li, Yuqing Liu, Zheng Liu, Philip S. Yu
TA-HGAT is built in a hyperbolic space to learn the hierarchical structure of session graphs.
no code implementations • 30 Dec 2022 • Qingyun Sun, JianXin Li, Beining Yang, Xingcheng Fu, Hao Peng, Philip S. Yu
Most Graph Neural Networks follow the message-passing paradigm, assuming the observed structure depicts the ground-truth node relationships.
1 code implementation • 29 Dec 2022 • Chunkai Zhang, Yuting Yang, Zilin Du, Wensheng Gan, Philip S. Yu
High-utility sequential pattern mining (HUSPM) has emerged as an important topic due to its wide application and considerable popularity.
no code implementations • 20 Dec 2022 • Gengsen Huang, Wensheng Gan, Philip S. Yu
An algorithm called Sequence Utility Maximization with Utility occupancy measure (SUMU) is proposed.
no code implementations • 8 Dec 2022 • Hengrui Zhang, Qitian Wu, Yu Wang, Shaofeng Zhang, Junchi Yan, Philip S. Yu
Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data.
1 code implementation • 1 Dec 2022 • Jiangshu Du, Wenpeng Yin, Congying Xia, Philip S. Yu
To deal with the two issues, this work first proposes a contextualized TE model (Context-TE) by appending other k options as the context of the current (P, H) modeling.
no code implementations • 30 Nov 2022 • Li Sun, Junda Ye, Hao Peng, Feiyang Wang, Philip S. Yu
On the one hand, existing methods work with the zero-curvature Euclidean space, and largely ignore the fact that curvature varies over the coming graph sequence.
1 code implementation • 18 Nov 2022 • Liangwei Yang, Shengjie Wang, Yunzhe Tao, Jiankai Sun, Xiaolong Liu, Philip S. Yu, Taiqing Wang
Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy.
1 code implementation • 14 Nov 2022 • Liangwei Yang, Shen Wang, Jibing Gong, Shaojie Zheng, Shuying Du, Zhiwei Liu, Philip S. Yu
To fill this gap, in this paper, we explore the rich, heterogeneous relationship among items and propose a new KG-enhanced recommendation model called Collaborative Meta-Knowledge Enhanced Recommender System (MetaKRec).
no code implementations • 11 Nov 2022 • Xuming Hu, Shiao Meng, Chenwei Zhang, Xiangli Yang, Lijie Wen, Irwin King, Philip S. Yu
Low-Resource Information Extraction (LRIE) strives to use unsupervised data, reducing the required resources and human annotation.
1 code implementation • 2 Nov 2022 • Mingdai Yang, Zhiwei Liu, Liangwei Yang, Xiaolong Liu, Chen Wang, Hao Peng, Philip S. Yu
PA layers efficiently learn the relatedness of non-neighbor nodes to improve the information propagation to users.
no code implementations • 28 Oct 2022 • Byung-Hak Kim, Zhongfen Deng, Philip S. Yu, Varun Ganapathi
The medical codes prediction problem from clinical notes has received substantial interest in the NLP community, and several recent studies have shown the state-of-the-art (SOTA) code prediction results of full-fledged deep learning-based methods.
1 code implementation • 24 Oct 2022 • Ziwei Fan, Zhiwei Liu, Chen Wang, Peijie Huang, Hao Peng, Philip S. Yu
However, it remains a significant challenge to model auxiliary item relationships in SR. To simultaneously model high-order item-item transitions in sequences and auxiliary item relationships, we propose a Multi-relational Transformer capable of modeling auxiliary item relationships for SR (MT4SR).
no code implementations • 19 Oct 2022 • Xuming Hu, Yong Jiang, Aiwei Liu, Zhongqiang Huang, Pengjun Xie, Fei Huang, Lijie Wen, Philip S. Yu
Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks).
no code implementations • 13 Oct 2022 • Jianpeng Chen, Yawen Ling, Jie Xu, Yazhou Ren, Shudong Huang, Xiaorong Pu, Zhifeng Hao, Philip S. Yu, Lifang He
The critical point of MGC is to better utilize the view-specific and view-common information in features and graphs of multiple views.
no code implementations • 9 Oct 2022 • Yazhou Ren, Jingyu Pu, Zhimeng Yang, Jie Xu, Guofeng Li, Xiaorong Pu, Philip S. Yu, Lifang He
Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering.
no code implementations • 27 Sep 2022 • Yao Chen, Wensheng Gan, Yongdong Wu, Philip S. Yu
Contrast pattern mining (CPM) is an important and popular subfield of data mining.
no code implementations • 27 Sep 2022 • Chunkai Zhang, Maohua Lyu, Wensheng Gan, Philip S. Yu
TotalSR creates a utility table that can efficiently calculate antecedent support and a utility prefix sum list that can compute the remaining utility in O(1) time for a sequence.
no code implementations • COLING 2022 • Xuming Hu, Zhijiang Guo, Yu Fu, Lijie Wen, Philip S. Yu
A scene graph is a semantic representation that expresses the objects, attributes, and relationships between objects in a scene.
1 code implementation • 4 Sep 2022 • Jiaqian Ren, Lei Jiang, Hao Peng, Lingjuan Lyu, Zhiwei Liu, Chaochao Chen, Jia Wu, Xu Bai, Philip S. Yu
Integrating multiple online social networks (OSNs) has important implications for many downstream social mining tasks, such as user preference modelling, recommendation, and link prediction.
no code implementations • 30 Aug 2022 • Li Sun, Junda Ye, Hao Peng, Philip S. Yu
To bridge this gap, we make the first attempt to study the problem of self-supervised temporal graph representation learning in the general Riemannian space, supporting the time-varying curvature to shift among hyperspherical, Euclidean and hyperbolic spaces.
no code implementations • 27 Aug 2022 • Jiahui Chen, Xu Guo, Wensheng Gan, Shichen Wan, Philip S. Yu
Compared with traditional utility mining, OSUM can find more practical and meaningful patterns in real-life applications.
1 code implementation • 27 Aug 2022 • Yu Wang, Hengrui Zhang, Zhiwei Liu, Liangwei Yang, Philip S. Yu
Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation.
1 code implementation • 17 Aug 2022 • Qingyun Sun, JianXin Li, Haonan Yuan, Xingcheng Fu, Hao Peng, Cheng Ji, Qian Li, Philip S. Yu
Topology-imbalance is a graph-specific imbalance problem caused by the uneven topology positions of labeled nodes, which significantly damages the performance of GNNs.
2 code implementations • 9 Aug 2022 • Ruitong Zhang, Hao Peng, Yingtong Dou, Jia Wu, Qingyun Sun, Jingyi Zhang, Philip S. Yu
DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality.
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 • 15 Jun 2022 • Yue Wang, Yao Wan, Lu Bai, Lixin Cui, Zhuo Xu, Ming Li, Philip S. Yu, Edwin R Hancock
To alleviate the challenges of building Knowledge Graphs (KG) from scratch, a more general task is to enrich a KG using triples from an open corpus, where the obtained triples contain noisy entities and relations.
no code implementations • 9 Jun 2022 • Wensheng Gan, Gengsen Huang, Jian Weng, Tianlong Gu, Philip S. Yu
In this paper, we provide the relevant definitions of target sequential rule and formulate the problem of targeted sequential rule mining.
1 code implementation • NAACL 2022 • Xuming Hu, Zhijiang Guo, Guanyu Wu, Aiwei Liu, Lijie Wen, Philip S. Yu
The explosion of misinformation spreading in the media ecosystem urges for automated fact-checking.
1 code implementation • 3 Jun 2022 • Yizhen Zheng, Shirui Pan, Vincent CS Lee, Yu Zheng, Philip S. Yu
Instead of similarity computation, GGD directly discriminates two groups of node samples with a very simple binary cross-entropy loss.
no code implementations • 31 May 2022 • Shu'ang Li, Xuming Hu, Li Lin, Aiwei Liu, Lijie Wen, Philip S. Yu
Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs (premise and hypothesis).
no code implementations • 24 May 2022 • Jiaqian Ren, Lei Jiang, Hao Peng, Zhiwei Liu, Jia Wu, Philip S. Yu
To incorporate temporal information into the message passing scheme, we introduce a novel temporal-aware aggregator which assigns weights to neighbours according to an adaptive time exponential decay formula.
1 code implementation • NAACL 2022 • Xuming Hu, Shuliang Liu, Chenwei Zhang, Shu`ang Li, Lijie Wen, Philip S. Yu
Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution.
1 code implementation • Findings (NAACL) 2022 • Yuwei Cao, William Groves, Tanay Kumar Saha, Joel R. Tetreault, Alex Jaimes, Hao Peng, Philip S. Yu
To date, work in this area has mostly focused on English as there is a scarcity of labeled data for other languages.
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.
no code implementations • 1 Apr 2022 • TingTing Liang, Yixuan Jiang, Congying Xia, Ziqiang Zhao, Yuyu Yin, Philip S. Yu
Recently, conversational OpenQA is proposed to address these issues with the abundant contextual information in the conversation.
1 code implementation • 25 Mar 2022 • Zhiwei Liu, Yongjun Chen, Jia Li, Man Luo, Philip S. Yu, Caiming Xiong
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
no code implementations • 18 Mar 2022 • Xusheng Zhao, Jia Wu, Hao Peng, Amin Beheshti, Jessica J. M. Monaghan, David Mcalpine, Heivet Hernandez-Perez, Mark Dras, Qiong Dai, Yangyang Li, Philip S. Yu, Lifang He
Modern neuroimaging techniques, such as diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), enable us to model the human brain as a brain network or connectome.
no code implementations • 12 Mar 2022 • Zhi-Hong Deng, Chang-Dong Wang, Ling Huang, Jian-Huang Lai, Philip S. Yu
G$^3$SR decomposes the session-based recommendation workflow into two steps.
1 code implementation • Findings (EMNLP) 2021 • Wenting Zhao, Ye Liu, Yao Wan, Philip S. Yu
Few-shot table-to-text generation is a task of composing fluent and faithful sentences to convey table content using limited data.
no code implementations • 26 Feb 2022 • Wensheng Gan, Guoting Chen, Hongzhi Yin, Philippe Fournier-Viger, Chien-Ming Chen, Philip S. Yu
To fulfill this gap, in this paper, we first propose a general profit-oriented framework to address the problem of revenue maximization based on economic behavior, and compute the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing.
no code implementations • 26 Feb 2022 • Gengsen Huang, Wensheng Gan, Philip S. Yu
What's more, to improve the efficiency of TaSPM on large-scale datasets and multiple-items-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in mining processes.
1 code implementation • 17 Feb 2022 • Sixiao Zhang, Hongxu Chen, Haoran Yang, Xiangguo Sun, Philip S. Yu, Guandong Xu
In this paper, we propose Graph Masked Autoencoders (GMAEs), a self-supervised transformer-based model for learning graph representations.
no code implementations • 14 Feb 2022 • Xin Zheng, Yi Wang, Yixin Liu, Ming Li, Miao Zhang, Di Jin, Philip S. Yu, Shirui Pan
In the end, we point out the potential directions to advance and stimulate more future research and applications on heterophilic graph learning with GNNs.
no code implementations • 8 Feb 2022 • Xiaoqin Pan, Xuan Lin, Dongsheng Cao, Xiangxiang Zeng, Philip S. Yu, Lifang He, Ruth Nussinov, Feixiong Cheng
Drug development is time-consuming and expensive.
1 code implementation • 7 Feb 2022 • Liangwei Yang, Zhiwei Liu, Yu Wang, Chen Wang, Ziwei Fan, Philip S. Yu
We conduct a comprehensive analysis of users' online game behaviors, which motivates the necessity of handling those three characteristics in the online game recommendation.
no code implementations • 25 Jan 2022 • Daokun Zhang, Jie Yin, Philip S. Yu
To generate informative node embeddings for link prediction, structural context prediction is leveraged as a self-supervised learning task to boost the link prediction performance.
no code implementations • 19 Jan 2022 • Haoran Yang, Hongxu Chen, Shirui Pan, Lin Li, Philip S. Yu, Guandong Xu
In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.
1 code implementation • 16 Jan 2022 • Ziwei Fan, Zhiwei Liu, Alice Wang, Zahra Nazari, Lei Zheng, Hao Peng, Philip S. Yu
We further argue that BPR loss has no constraint on positive and sampled negative items, which misleads the optimization.
no code implementations • 16 Jan 2022 • Ziwen Du, Ning Yang, Zhonghua Yu, Philip S. Yu
To address this challenges, we propose a novel model called Temporary Interest Aware Recommendation (TIARec), which can distinguish atypical interactions from normal ones without supervision and capture the temporary interest as well as the general preference of users.
no code implementations • 16 Jan 2022 • Xiaoyun Zhao, Ning Yang, Philip S. Yu
Meanwhile, we propose a Multi-Domain Adaptation Network (MDAN) for MSDCR to capture a user's domain-invariant aspect preference.
no code implementations • 15 Jan 2022 • Yuefei Lyu, Xiaoyu Yang, Jiaxin Liu, Philip S. Yu, Sihong Xie, Xi Zhang
To discover subtle vulnerabilities, we design a powerful attacking algorithm to camouflage rumors in social networks based on reinforcement learning that can interact with and attack any black-box detectors.
1 code implementation • 16 Dec 2021 • Qingyun Sun, JianXin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, Philip S. Yu
Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.
no code implementations • 14 Dec 2021 • Yiqi Wang, Chaozhuo Li, Zheng Liu, Mingzheng Li, Jiliang Tang, Xing Xie, Lei Chen, Philip S. Yu
Thus, graph pre-training has the great potential to alleviate data sparsity in GNN-based recommendations.
no code implementations • 10 Dec 2021 • Li Sun, Zhongbao Zhang, Junda Ye, Hao Peng, Jiawei Zhang, Sen Su, Philip S. Yu
Instead of working on one single constant-curvature space, we construct a mixed-curvature space via the Cartesian product of multiple Riemannian component spaces and design hierarchical attention mechanisms for learning and fusing the representations across these component spaces.
no code implementations • 29 Nov 2021 • Gengsen Huang, Wensheng Gan, Jian Weng, Philip S. Yu
High utility sequential pattern mining (HUSPM) is one kind of utility-driven mining.
no code implementations • 28 Nov 2021 • Xiaohan Li, Zhiwei Liu, Stephen Guo, Zheng Liu, Hao Peng, Philip S. Yu, Kannan Achan
In this paper, we propose a novel Reinforced Attentive Multi-relational Graph Neural Network (RAM-GNN) to the pre-train user and item embeddings on the user and item graph prior to the recommendation step.
no code implementations • 25 Nov 2021 • Chuanpan Zheng, Xiaoliang Fan, Shirui Pan, Haibing Jin, Zhaopeng Peng, Zonghan Wu, Cheng Wang, Philip S. Yu
However, this approach failed to explicitly reflect the correlations between different nodes at different time steps, thus limiting the learning capability of graph neural networks.
no code implementations • 24 Nov 2021 • Yicong Li, Hongxu Chen, Yile Li, Lin Li, Philip S. Yu, Guandong Xu
Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs.
no code implementations • 21 Nov 2021 • Zhiwei Liu, Liangwei Yang, Ziwei Fan, Hao Peng, Philip S. Yu
However, they all require centralized storage of the social links and item interactions of users, which leads to privacy concerns.
no code implementations • 16 Nov 2021 • Chen Wang, Yueqing Liang, Zhiwei Liu, Tao Zhang, Philip S. Yu
Then, we transfer the pre-trained graph encoder to initialize the node embeddings on the target domain, which benefits the fine-tuning of the single domain recommender system on the target domain.
no code implementations • 29 Oct 2021 • Nooshin Mojab, Philip S. Yu, Joelle A. Hallak, Darvin Yi
The success of deep learning methods relies heavily on the availability of a large amount of data.
1 code implementation • Findings (EMNLP) 2021 • Ye Liu, Kazuma Hashimoto, Yingbo Zhou, Semih Yavuz, Caiming Xiong, Philip S. Yu
In this work, we propose Dense Hierarchical Retrieval (DHR), a hierarchical framework that can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic semantics specific to each passage.
1 code implementation • 15 Oct 2021 • Xingcheng Fu, JianXin Li, Jia Wu, Qingyun Sun, Cheng Ji, Senzhang Wang, Jiajun Tan, Hao Peng, Philip S. Yu
Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.
no code implementations • 14 Oct 2021 • Yang Shu, Zhangjie Cao, Jinghan Gao, Jianmin Wang, Philip S. Yu, Mingsheng Long
While pre-training and meta-training can create deep models powerful for few-shot generalization, we find that pre-training and meta-training focuses respectively on cross-domain transferability and cross-task transferability, which restricts their data efficiency in the entangled settings of domain shift and task shift.
1 code implementation • 13 Oct 2021 • Jiangshu Du, Yingtong Dou, Congying Xia, Limeng Cui, Jing Ma, Philip S. Yu
The COVID-19 pandemic poses a great threat to global public health.
1 code implementation • EMNLP 2021 • Ye Liu, Jian-Guo Zhang, Yao Wan, Congying Xia, Lifang He, Philip S. Yu
To capture the semantic graph structure from raw text, most existing summarization approaches are built on GNNs with a pre-trained model.
no code implementations • 4 Oct 2021 • Chen Wang, Yingtong Dou, Min Chen, Jia Chen, Zhiwei Liu, Philip S. Yu
The successes of most previous methods heavily rely on rich node features and high-fidelity labels.
no code implementations • 29 Sep 2021 • Zhiwei Liu, Yongjun Chen, Jia Li, Man Luo, Philip S. Yu, Caiming Xiong
However, existing methods all construct views by adopting augmentation from data perspectives, while we argue that 1) optimal data augmentation methods are hard to devise, 2) data augmentation methods destroy sequential correlations, and 3) data augmentation fails to incorporate comprehensive self-supervised signals.
no code implementations • 29 Sep 2021 • Hengrui Zhang, Qitian Wu, Shaofeng Zhang, Junchi Yan, David Wipf, Philip S. Yu
In this paper, we propose ESCo (Effective and Scalable Contrastive), a new contrastive framework which is essentially an instantiation of the Information Bottleneck principle under self-supervised learning settings.
1 code implementation • EMNLP 2021 • Xuming Hu, Chenwei Zhang, Yawen Yang, Xiaohe Li, Li Lin, Lijie Wen, Philip S. Yu
Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.
no code implementations • 7 Sep 2021 • Haoran Yang, Hongxu Chen, Lin Li, Philip S. Yu, Guandong Xu
They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks.
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 • 14 Aug 2021 • Zhiwei Liu, Yongjun Chen, Jia Li, Philip S. Yu, Julian McAuley, Caiming Xiong
In this paper, we investigate the application of contrastive Self-Supervised Learning (SSL) to the sequential recommendation, as a way to alleviate some of these issues.
1 code implementation • 14 Aug 2021 • Ziwei Fan, Zhiwei Liu, Jiawei Zhang, Yun Xiong, Lei Zheng, Philip S. Yu
Therefore, we propose to unify sequential patterns and temporal collaborative signals to improve the quality of recommendation, which is rather challenging.
no code implementations • 5 Jul 2021 • Qian Li, JianXin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu
Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey.
no code implementations • 30 Jun 2021 • Qiaomin Yi, Ning Yang, Philip S. Yu
First, the noise injection based methods often draw the noise from a fixed noise distribution given in advance, while in real world, the noise distributions of different users and items may differ from each other due to personal behaviors and item usage patterns.
1 code implementation • NeurIPS 2021 • Hengrui Zhang, Qitian Wu, Junchi Yan, David Wipf, Philip S. Yu
We introduce a conceptually simple yet effective model for self-supervised representation learning with graph data.
1 code implementation • 23 Jun 2021 • Qian Li, Hao Peng, JianXin Li, Jia Wu, Yuanxing Ning, Lihong Wang, Philip S. Yu, Zheng Wang
Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually.
1 code implementation • 11 Jun 2021 • Ziwei Fan, Zhiwei Liu, Lei Zheng, Shen Wang, Philip S. Yu
We use Elliptical Gaussian distributions to describe items and sequences with uncertainty.
1 code implementation • 8 Jun 2021 • Siddharth Bhatia, Mohit Wadhwa, Kenji Kawaguchi, Neil Shah, Philip S. Yu, Bryan Hooi
This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure).
1 code implementation • 8 Jun 2021 • JianGuo Zhang, Kazuma Hashimoto, Yao Wan, Zhiwei Liu, Ye Liu, Caiming Xiong, Philip S. Yu
Pre-trained Transformer-based models were reported to be robust in intent classification.
no code implementations • 26 May 2021 • Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cecile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu
A community reveals the features and connections of its members that are different from those in other communities in a network.
1 code implementation • 22 May 2021 • JianXin Li, Xingcheng Fu, Hao Peng, Senzhang Wang, Shijie Zhu, Qingyun Sun, Philip S. Yu, Lifang He
With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.
1 code implementation • 13 May 2021 • Shoujin Wang, Liang Hu, Yan Wang, Xiangnan He, Quan Z. Sheng, Mehmet A. Orgun, Longbing Cao, Francesco Ricci, Philip S. Yu
Recent years have witnessed the fast development of the emerging topic of Graph Learning based Recommender Systems (GLRS).
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 • 7 May 2021 • Mehrnaz Najafi, Lifang He, Philip S. Yu
Due to inevitable sensor failures, data in each view may contain error.
1 code implementation • 2 May 2021 • Zhiwei Liu, Ziwei Fan, Yu Wang, Philip S. Yu
We firstly pre-train a transformer with sequences in a reverse direction to predict prior items.
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 • 16 Apr 2021 • JianXin Li, Hao Peng, Yuwei Cao, Yingtong Dou, Hekai Zhang, Philip S. Yu, Lifang He
Furthermore, they cannot fully capture the content-based correlations between nodes, as they either do not use the self-attention mechanism or only use it to consider the immediate neighbors of each node, ignoring the higher-order neighbors.
1 code implementation • 16 Apr 2021 • Hao Peng, Ruitong Zhang, Yingtong Dou, Renyu Yang, Jingyi Zhang, Philip S. Yu
To avoid the embedding over-assimilation among different types of nodes, we employ a label-aware neural similarity measure to ascertain the most similar neighbors based on node attributes.
Ranked #3 on Node Classification on Amazon-Fraud
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 • Zhongfen Deng, Hao Peng, Dongxiao He, JianXin Li, Philip S. Yu
The second one encourages the structure encoder to learn better representations with desired characteristics for all labels which can better handle label imbalance in hierarchical text classification.
no code implementations • 6 Apr 2021 • Li Sun, Zhongbao Zhang, Jiawei Zhang, Feiyang Wang, Hao Peng, Sen Su, Philip S. Yu
To model the uncertainty, we devise a hyperbolic graph variational autoencoder built upon the proposed TGNN to generate stochastic node representations of hyperbolic normal distributions.
1 code implementation • 2 Apr 2021 • Hao Peng, JianXin Li, Yangqiu Song, Renyu Yang, Rajiv Ranjan, Philip S. Yu, Lifang He
Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method.
no code implementations • 30 Mar 2021 • Nooshin Mojab, Vahid Noroozi, Abdullah Aleem, Manoj P. Nallabothula, Joseph Baker, Dimitri T. Azar, Mark Rosenblatt, RV Paul Chan, Darvin Yi, Philip S. Yu, Joelle A. Hallak
In this paper, we present a new multi-modal longitudinal ophthalmic imaging dataset, the Illinois Ophthalmic Database Atlas (I-ODA), with the goal of advancing state-of-the-art computer vision applications in ophthalmology, and improving upon the translatable capacity of AI based applications across different clinical settings.