no code implementations • ICML 2020 • Cheng Zheng, Bo Zong, Wei Cheng, Dongjin Song, Jingchao Ni, Wenchao Yu, Haifeng Chen, Wei Wang
Graph representation learning serves as the core of important prediction tasks, ranging from product recommendation to fraud detection.
no code implementations • 18 Feb 2024 • Fali Wang, Runxue Bao, Suhang Wang, Wenchao Yu, Yanchi Liu, Wei Cheng, Haifeng Chen
Though Large Language Models (LLMs) have shown remarkable open-generation capabilities across diverse domains, they struggle with knowledge-intensive tasks.
no code implementations • 3 Oct 2023 • Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Haifeng Chen, Wei Wang, Wei Cheng
To address this challenge, we introduce Privacy Protection Language Models (PPLM), a novel paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding data privacy.
no code implementations • 30 Sep 2023 • Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen
After the model is learned, we can obtain causal relations among states and action variables behind its decisions, exposing policies learned by it.
1 code implementation • 12 Sep 2023 • Yufei Li, Yanchi Liu, Haoyu Wang, Zhengzhang Chen, Wei Cheng, Yuncong Chen, Wenchao Yu, Haifeng Chen, Cong Liu
Subsequently, GLAD utilizes a temporal-attentive graph edge anomaly detection model for identifying anomalous relations in these dynamic log graphs.
1 code implementation • 13 Jun 2023 • Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang, Xiang Zhang, Yuncong Chen, Yanchi Liu, Wei Cheng, Haifeng Chen
Imitation learning has achieved great success in many sequential decision-making tasks, in which a neural agent is learned by imitating collected human demonstrations.
1 code implementation • 1 May 2023 • Yue Wu, Shuaicheng Zhang, Wenchao Yu, Yanchi Liu, Quanquan Gu, Dawei Zhou, Haifeng Chen, Wei Cheng
The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while maintaining data privacy.
1 code implementation • 21 Mar 2023 • Dongsheng Luo, Wei Cheng, Yingheng Wang, Dongkuan Xu, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Yanchi Liu, Yuncong Chen, Haifeng Chen, Xiang Zhang
A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples, such that an encoder can be trained to learn robust and discriminative representations.
1 code implementation • 6 Mar 2023 • Xianjun Yang, Wei Cheng, Xujiang Zhao, Wenchao Yu, Linda Petzold, Haifeng Chen
Experimental results underscore the significant performance improvement achieved by dynamic prompt tuning across a wide range of tasks, including NLP tasks, vision recognition tasks, and vision-language tasks.
1 code implementation • 26 Oct 2022 • Tianchun Wang, Wei Cheng, Dongsheng Luo, Wenchao Yu, Jingchao Ni, Liang Tong, Haifeng Chen, Xiang Zhang
Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention.
no code implementations • 5 Feb 2022 • Xujiang Zhao, Xuchao Zhang, Wei Cheng, Wenchao Yu, Yuncong Chen, Haifeng Chen, Feng Chen
Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes.
no code implementations • 29 Sep 2021 • Dongsheng Luo, Wei Cheng, Yingheng Wang, Dongkuan Xu, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Yanchi Liu, Haifeng Chen, Xiang Zhang
How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question.
1 code implementation • 26 Mar 2021 • Dongsheng Luo, Wei Cheng, Jingchao Ni, Wenchao Yu, Xuchao Zhang, Bo Zong, Yanchi Liu, Zhengzhang Chen, Dongjin Song, Haifeng Chen, Xiang Zhang
We present a contrasting learning approach with data augmentation techniques to learn document representations in an unsupervised manner.
1 code implementation • 15 Mar 2021 • Junheng Hao, Muhao Chen, Wenchao Yu, Yizhou Sun, Wei Wang
The cross-view association model is learned to bridge the embeddings of ontological concepts and their corresponding instance-view entities.
no code implementations • ICCV 2021 • Yao Li, Martin Renqiang Min, Thomas Lee, Wenchao Yu, Erik Kruus, Wei Wang, Cho-Jui Hsieh
Recent studies have demonstrated the vulnerability of deep neural networks against adversarial examples.
no code implementations • 1 Jan 2021 • Lichen Wang, Bo Zong, Yunyu Liu, Can Qin, Wei Cheng, Wenchao Yu, Xuchao Zhang, Haifeng Chen, Yun Fu
As texts always contain a large proportion of task-irrelevant words, accurate alignment between aspects and their sentimental descriptions is the most crucial and challenging step.
1 code implementation • 13 Nov 2020 • Dongsheng Luo, Wei Cheng, Wenchao Yu, Bo Zong, Jingchao Ni, Haifeng Chen, Xiang Zhang
Graph Neural Networks (GNNs) have shown to be powerful tools for graph analytics.
3 code implementations • NeurIPS 2020 • Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, Xiang Zhang
The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of the learned GNN model, leading to a lack of generalizability and hindering it from being used in the inductive setting.
no code implementations • ICLR 2020 • Lichen Wang, Bo Zong, Qianqian Ma, Wei Cheng, Jingchao Ni, Wenchao Yu, Yanchi Liu, Dongjin Song, Haifeng Chen, Yun Fu
Inductive and unsupervised graph learning is a critical technique for predictive or information retrieval tasks where label information is difficult to obtain.
no code implementations • 4 Oct 2019 • Lu Wang, Wenchao Yu, Wei Wang, Wei Cheng, Wei zhang, Hongyuan Zha, Xiaofeng He, Haifeng Chen
Graph representation learning, aiming to learn low-dimensional representations which capture the geometric dependencies between nodes in the original graph, has gained increasing popularity in a variety of graph analysis tasks, including node classification and link prediction.
no code implementations • 25 Sep 2019 • Yao Li, Martin Renqiang Min, Wenchao Yu, Cho-Jui Hsieh, Thomas Lee, Erik Kruus
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples.
no code implementations • 19 Nov 2018 • Yao Li, Martin Renqiang Min, Wenchao Yu, Cho-Jui Hsieh, Thomas C. M. Lee, Erik Kruus
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples.
1 code implementation • ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 • Wenchao Yu, Cheng Zheng, Wei Cheng, Charu C. Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, Wei Wang
The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure.