no code implementations • NAACL 2022 • Hao Huang, Xiubo Geng, Guodong Long, Daxin Jiang
Precise question understanding is critical for temporal reading comprehension.
1 code implementation • 26 May 2024 • Shutong Chen, Tianyi Zhou, Guodong Long, Jie Ma, Jing Jiang, Chengqi Zhang
For every mid-level, it learns multiple models each assigned to a subgroup of clients, as clustered FL.
1 code implementation • 8 May 2024 • Chunxu Zhang, Guodong Long, Hongkuan Guo, Xiao Fang, Yang song, Zhaojie Liu, Guorui Zhou, Zijian Zhang, Yang Liu, Bo Yang
It becomes a new open challenge to enable the foundation model to capture user preference changes in a timely manner with reasonable communication and computation costs while preserving privacy.
1 code implementation • 16 Apr 2024 • Zhihong Deng, Jing Jiang, Guodong Long, Chengqi Zhang
In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maximizing returns.
no code implementations • 28 Mar 2024 • Yiyuan Yang, Guodong Long, Tao Shen, Jing Jiang, Michael Blumenstein
To address challenges in this new setting, we explore a simple yet effective solution to learn a comprehensive foundation model.
no code implementations • 28 Mar 2024 • Peng Yan, Guodong Long
Personalized Federated Learning (PerFL) is a new machine learning paradigm that delivers personalized models for diverse clients under federated learning settings.
1 code implementation • 5 Dec 2023 • Shengchao Chen, Guodong Long, Jing Jiang, Dikai Liu, Chengqi Zhang
Furthermore, in relation to the creation and application of foundation models for weather and climate data understanding, we delve into the field's prevailing challenges, offer crucial insights, and propose detailed avenues for future research.
no code implementations • 15 Nov 2023 • Yucheng Zhou, Xiubo Geng, Tao Shen, Chongyang Tao, Guodong Long, Jian-Guang Lou, Jianbing Shen
Large Language Models (LLMs) have ushered in a transformative era in the field of natural language processing, excelling in tasks related to text comprehension and generation.
no code implementations • 21 Sep 2023 • Shuang Ao, Tianyi Zhou, Guodong Long, Xuan Song, Jing Jiang
Throughout long history, natural species have learned to survive by evolving their physical structures adaptive to the environment changes.
1 code implementation • 12 Sep 2023 • Xiaohan Xu, Chongyang Tao, Tao Shen, Can Xu, Hongbo Xu, Guodong Long, Jian-Guang Lou
To enhance the reasoning capabilities of off-the-shelf Large Language Models (LLMs), we introduce a simple, yet general and effective prompting method, Re2, i. e., \textbf{Re}-\textbf{Re}ading the question as input.
no code implementations • 10 Jul 2023 • Haiyan Zhao, Guodong Long
Large-scale pre-trained models have been remarkably successful in resolving downstream tasks.
no code implementations • 4 Jul 2023 • Zhihong Deng, Jing Jiang, Guodong Long, Chengqi Zhang
Causality, however, offers a notable advantage as it can formalize knowledge in a systematic manner and leverage invariance for effective knowledge transfer.
no code implementations • 6 Jun 2023 • Peng Yan, Guodong Long
Personalized federated learning (PFL) jointly trains a variety of local models through balancing between knowledge sharing across clients and model personalization per client.
1 code implementation • 1 Jun 2023 • Xiuying Chen, Guodong Long, Chongyang Tao, Mingzhe Li, Xin Gao, Chengqi Zhang, Xiangliang Zhang
The other factor is in the latent space, where the attacked inputs bring more variations to the hidden states.
1 code implementation • 29 May 2023 • Yijun Yang, Tianyi Zhou, Jing Jiang, Guodong Long, Yuhui Shi
We address it by "Continual Task Allocation via Sparse Prompting (CoTASP)", which learns over-complete dictionaries to produce sparse masks as prompts extracting a sub-network for each task from a meta-policy network.
no code implementations • 23 May 2023 • Shengchao Chen, Guodong Long, Tao Shen, Jing Jiang, Chengqi Zhang
On-device intelligence for weather forecasting uses local deep learning models to analyze weather patterns without centralized cloud computing, holds significance for supporting human activates.
no code implementations • 22 May 2023 • Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijian Zhang, Peng Yan, Bo Yang
However, this separation of the recommendation model and users' private data poses a challenge in providing quality service, particularly when it comes to new items, namely cold-start recommendations in federated settings.
no code implementations • 13 May 2023 • Chunxu Zhang, Guodong Long, Tianyi Zhou, Peng Yan, Zijjian Zhang, Bo Yang
Federated Recommendation is a new service architecture providing recommendations without sharing user data with the server.
1 code implementation • 12 May 2023 • Jiazhan Feng, Chongyang Tao, Xiubo Geng, Tao Shen, Can Xu, Guodong Long, Dongyan Zhao, Daxin Jiang
Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs).
no code implementations • 27 Apr 2023 • Tao Shen, Guodong Long, Xiubo Geng, Chongyang Tao, Tianyi Zhou, Daxin Jiang
In this work, we propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios.
no code implementations • 9 Apr 2023 • Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
In this paper, we study which modules in neural networks are more prone to forgetting by investigating their training dynamics during CL.
no code implementations • 4 Feb 2023 • Kun Yi, Qi Zhang, Longbing Cao, Shoujin Wang, Guodong Long, Liang Hu, Hui He, Zhendong Niu, Wei Fan, Hui Xiong
Despite the growing attention and the proliferation of research in this emerging field, there is currently a lack of a systematic review and in-depth analysis of deep learning-based time series models with FT.
no code implementations • 27 Jan 2023 • Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
To address these challenges, we create a small model for a new task from the pruned models of similar tasks.
no code implementations • 26 Jan 2023 • Yucheng Zhou, Guodong Long
Specifically, we construct visual and semantic event graphs from story plots and ending image, and leverage event-based reasoning to reason and mine implicit information in a single modality.
no code implementations • 26 Jan 2023 • Yucheng Zhou, Guodong Long
Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image.
no code implementations • 26 Jan 2023 • Yucheng Zhou, Guodong Long
Existing multi-style image captioning methods show promising results in generating a caption with accurate visual content and desired linguistic style.
1 code implementation • 22 Jan 2023 • Shengchao Chen, Guodong Long, Tao Shen, Jing Jiang
To relieve the data exposure concern across regions, a novel federated learning approach has been proposed to collaboratively learn a brand-new spatio-temporal Transformer-based foundation model across participants with heterogeneous meteorological data.
1 code implementation • 22 Jan 2023 • Zhiwei Li, Guodong Long, Tianyi Zhou
To address these challenges, we propose Federated Recommendation with Additive Personalization (FedRAP), which learns a global view of items via FL and a personalized view locally on each user.
1 code implementation • 16 Jan 2023 • Chunxu Zhang, Guodong Long, Tianyi Zhou, Peng Yan, Zijian Zhang, Chengqi Zhang, Bo Yang
Moreover, we provide visualizations and in-depth analysis of the personalization techniques in item embedding, which shed novel insights on the design of recommender systems in federated settings.
no code implementations • 20 Dec 2022 • Yucheng Zhou, Tao Shen, Xiubo Geng, Chongyang Tao, Guodong Long, Can Xu, Daxin Jiang
Long document retrieval aims to fetch query-relevant documents from a large-scale collection, where knowledge distillation has become de facto to improve a retriever by mimicking a heterogeneous yet powerful cross-encoder.
1 code implementation • 23 Nov 2022 • Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, Chengqi Zhang
Inspired by this, we propose FedStar, an FGL framework that extracts and shares the common underlying structure information for inter-graph federated learning tasks.
no code implementations • 11 Nov 2022 • Yang Li, Canran Xu, Guodong Long, Tao Shen, Chongyang Tao, Jing Jiang
Basically, an instance-dependent soft prefix, derived from fact-counterfactual pairs in the label space, is leveraged to complement the language verbalizers in many-class classification.
no code implementations • 27 Oct 2022 • Chenglin Wang, Yucheng Zhou, Guodong Long, Xiaodong Wang, Xiaowei Xu
Therefore, we propose an unsupervised knowledge graph construction method to build a scientific knowledge graph (SKG) without any labeled data.
2 code implementations • 21 Sep 2022 • Yue Tan, Guodong Long, Jie Ma, Lu Liu, Tianyi Zhou, Jing Jiang
To prevent these issues from hindering the deployment of FL systems, we propose a lightweight framework where clients jointly learn to fuse the representations generated by multiple fixed pre-trained models rather than training a large-scale model from scratch.
no code implementations • 16 Jun 2022 • Yucheng Zhou, Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Guodong Long, Binxing Jiao, Daxin Jiang
A ranker plays an indispensable role in the de facto 'retrieval & rerank' pipeline, but its training still lags behind -- learning from moderate negatives or/and serving as an auxiliary module for a retriever.
no code implementations • 23 May 2022 • Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Guodong Long, Kai Zhang, Daxin Jiang
Large-scale retrieval is to recall relevant documents from a huge collection given a query.
no code implementations • 20 May 2022 • Zhuowei Wang, Tianyi Zhou, Guodong Long, Bo Han, Jing Jiang
Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices.
1 code implementation • ACL 2022 • Yucheng Zhou, Tao Shen, Xiubo Geng, Guodong Long, Daxin Jiang
Generating new events given context with correlated ones plays a crucial role in many event-centric reasoning tasks.
1 code implementation • 2 Mar 2022 • Fengwen Chen, Guodong Long, Zonghan Wu, Tianyi Zhou, Jing Jiang
We propose a novel structured federated learning (SFL) framework to learn both the global and personalized models simultaneously using client-wise relation graphs and clients' private data.
1 code implementation • 13 Feb 2022 • Jie Ma, Guodong Long, Tianyi Zhou, Jing Jiang, Chengqi Zhang
Knowledge sharing and model personalization are essential components to tackle the non-IID challenge in federated learning (FL).
1 code implementation • NeurIPS 2021 • Shuang Ao, Tianyi Zhou, Guodong Long, Qinghua Lu, Liming Zhu, Jing Jiang
Next, a bottom-up traversal of the tree trains the RL agent from easier sub-tasks with denser rewards on bottom layers to harder ones on top layers and collects its cost on each sub-task train the planner in the next episode.
no code implementations • 13 Oct 2021 • Yucheng Zhou, Xiubo Geng, Tao Shen, Guodong Long, Daxin Jiang
Event correlation reasoning infers whether a natural language paragraph containing multiple events conforms to human common sense.
no code implementations • 29 Sep 2021 • Han Zheng, Jing Jiang, Pengfei Wei, Guodong Long, Xuan Song, Chengqi Zhang
URPL adds an uncertainty regularization term in the policy learning objective to enforce to learn a more stable policy under the offline setting.
no code implementations • 29 Sep 2021 • Shuang Ao, Tianyi Zhou, Jing Jiang, Guodong Long, Xuan Song, Chengqi Zhang
They are complementary in acquiring more informative feedback for RL: the planning policy provides dense reward of finishing easier sub-tasks while the environment policy modifies these sub-tasks to be adequately challenging and diverse so the RL agent can quickly adapt to different tasks/environments.
no code implementations • 29 Sep 2021 • Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Liming Zhu, Chengqi Zhang
Can we find a better initialization for a new task, e. g., a much smaller network closer to the final pruned model, by exploiting its similar tasks?
no code implementations • Findings (NAACL) 2022 • Yang Li, Guodong Long, Tao Shen, Jing Jiang
It consists of (1) a pairwise type-enriched sentence encoding module injecting both context-free and -related backgrounds to alleviate sentence-level wrong labeling, and (2) a hierarchical type-sentence alignment module enriching a sentence with the triple fact's basic attributes to support long-tail relations.
1 code implementation • 7 Sep 2021 • Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang
Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven crucial for predictive analytics in the medical domain.
1 code implementation • Findings (EMNLP) 2021 • Bo wang, Tao Shen, Guodong Long, Tianyi Zhou, Yi Chang
Aspect-level sentiment classification (ALSC) aims at identifying the sentiment polarity of a specified aspect in a sentence.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +3
1 code implementation • 25 Aug 2021 • Zonghan Wu, Da Zheng, Shirui Pan, Quan Gan, Guodong Long, George Karypis
This paper aims to unify spatial dependency and temporal dependency in a non-Euclidean space while capturing the inner spatial-temporal dependencies for traffic data.
no code implementations • 24 Aug 2021 • Guodong Long, Tao Shen, Yue Tan, Leah Gerrard, Allison Clarke, Jing Jiang
Implementing an open innovation framework in the healthcare industry, namely open health, is to enhance innovation and creative capability of health-related organisations by building a next-generation collaborative framework with partner organisations and the research community.
no code implementations • 24 Aug 2021 • Guodong Long, Yue Tan, Jing Jiang, Chengqi Zhang
In the near future, it is foreseeable to have decentralized data ownership in the finance sector using federated learning.
1 code implementation • 19 Aug 2021 • Guodong Long, Ming Xie, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang, Chengqi Zhang
By comparison, a mixture of multiple global models could capture the heterogeneity across various clients if assigning the client to different global models (i. e., centers) in FL.
no code implementations • ACL 2021 • Hao Huang, Xiubo Geng, Jian Pei, Guodong Long, Daxin Jiang
Procedural text understanding aims at tracking the states (e. g., create, move, destroy) and locations of the entities mentioned in a given paragraph.
1 code implementation • 20 Jul 2021 • Xueping Peng, Guodong Long, Sen Wang, Jing Jiang, Allison Clarke, Clement Schlegel, Chengqi Zhang
Hence, some recent works train healthcare representations by incorporating medical ontology, by self-supervised tasks like diagnosis prediction, but (1) the small-scale, monotonous ontology is insufficient for robust learning, and (2) critical contexts or dependencies underlying patient journeys are barely exploited to enhance ontology learning.
1 code implementation • 10 Jul 2021 • Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang
Second, the bandwidth of existing graph convolutional filters is fixed.
4 code implementations • 1 May 2021 • Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, Chengqi Zhang
Heterogeneity across clients in federated learning (FL) usually hinders the optimization convergence and generalization performance when the aggregation of clients' knowledge occurs in the gradient space.
no code implementations • 3 Mar 2021 • Zhe Liu, Yun Li, Lina Yao, Xianzhi Wang, Guodong Long
Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen).
no code implementations • 25 Feb 2021 • Shaoxiong Ji, Yue Tan, Teemu Saravirta, Zhiqin Yang, Yixin Liu, Lauri Vasankari, Shirui Pan, Guodong Long, Anwar Walid
Federated learning is a new learning paradigm that decouples data collection and model training via multi-party computation and model aggregation.
no code implementations • ICLR 2021 • Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Xuanyi Dong, Chengqi Zhang
To resolve this problem, we propose Isometric Propagation Network (IPN), which learns to strengthen the relation between classes within each space and align the class dependency in the two spaces.
no code implementations • 24 Jan 2021 • Xiaohan Zhang, Lu Liu, Guodong Long, Jing Jiang, Shenquan Liu
Typical methods to study cognitive function are to record the electrical activities of animal neurons during the training of animals performing behavioral tasks.
no code implementations • 1 Jan 2021 • Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
In this paper, we introduce an efficient method, \name, to extract the local inference chains by optimizing a differentiable sparse scoring for the filters and layers to preserve the outputs on given data from a local region.
no code implementations • 1 Jan 2021 • Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Xuanyi Dong, Chengqi Zhang
Few-shot learning aims to train a classifier given only a few samples per class that are highly insufficient to describe the whole data distribution.
no code implementations • 2 Dec 2020 • Zhuowei Wang, Jing Jiang, Bo Han, Lei Feng, Bo An, Gang Niu, Guodong Long
We also instantiate our framework with different combinations, which set the new state of the art on benchmark-simulated and real-world datasets with noisy labels.
no code implementations • 6 Nov 2020 • Bingcong Li, Bo Han, Zhuowei Wang, Jing Jiang, Guodong Long
Specifically, our method maintains a dynamically updating confusion matrix, which analyzes confusable classes in the dataset.
no code implementations • NeurIPS 2020 • Han Zheng, Pengfei Wei, Jing Jiang, Guodong Long, Qinghua Lu, Chengqi Zhang
Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws.
no code implementations • COLING 2020 • Hao Huang, Guodong Long, Tao Shen, Jing Jiang, Chengqi Zhang
Many graph embedding approaches have been proposed for knowledge graph completion via link prediction.
2 code implementations • COLING 2020 • Yang Li, Tao Shen, Guodong Long, Jing Jiang, Tianyi Zhou, Chengqi Zhang
Then, facilitated by the proposed base model, we introduce collaborating relation features shared among relations in the hierarchies to promote the relation-augmenting process and balance the training data for long-tail relations.
1 code implementation • 24 Sep 2020 • Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang, Chengqi Zhang
Electronic health records (EHRs) are longitudinal records of a patient's interactions with healthcare systems.
no code implementations • 24 Sep 2020 • Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
To address this challenging task, most ZSL methods relate unseen test classes to seen(training) classes via a pre-defined set of attributes that can describe all classes in the same semantic space, so the knowledge learned on the training classes can be adapted to unseen classes.
1 code implementation • 28 Jun 2020 • Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings.
1 code implementation • ICLR 2021 • Lu Liu, William Hamilton, Guodong Long, Jing Jiang, Hugo Larochelle
We consider the problem of multi-domain few-shot image classification, where unseen classes and examples come from diverse data sources.
Ranked #1 on Few-Shot Image Classification on Meta-Dataset Rank
1 code implementation • 15 Jun 2020 • Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang
The key challenge of patient journey understanding is to design an effective encoding mechanism which can properly tackle the aforementioned multi-level structured patient journey data with temporal sequential visits and a set of medical codes.
1 code implementation • 3 Jun 2020 • Wensi Tang, Lu Liu, Guodong Long
Recent few-shot learning works focus on training a model with prior meta-knowledge to fast adapt to new tasks with unseen classes and samples.
2 code implementations • 24 May 2020 • Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, Chengqi Zhang
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic.
Ranked #1 on Univariate Time Series Forecasting on Electricity
3 code implementations • 3 May 2020 • Guodong Long, Ming Xie, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang, Chengqi Zhang
However, due to the diverse nature of user behaviors, assigning users' gradients to different global models (i. e., centers) can better capture the heterogeneity of data distributions across users.
1 code implementation • 30 Apr 2020 • Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou, Yi Chang
In experiments, we achieve state-of-the-art performance on three benchmarks and a zero-shot dataset for link prediction, with highlights of inference costs reduced by 1-2 orders of magnitude compared to a textual encoding method.
Ranked #4 on Link Prediction on UMLS
no code implementations • EMNLP 2020 • Tao Shen, Yi Mao, Pengcheng He, Guodong Long, Adam Trischler, Weizhu Chen
In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training, to inject language models with structured knowledge via learning from raw text.
3 code implementations • ICLR 2022 • Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Michael Blumenstein, Jing Jiang
Particularly, it is a set of kernel sizes that can efficiently cover the best RF size across different datasets via consisting of multiple prime numbers according to the length of the time series.
no code implementations • 27 Nov 2019 • Yang Li, Guodong Long, Tao Shen, Tianyi Zhou, Lina Yao, Huan Huo, Jing Jiang
Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision.
no code implementations • 23 Oct 2019 • Shaoxiong Ji, Shirui Pan, Xue Li, Erik Cambria, Guodong Long, Zi Huang
Suicide is a critical issue in modern society.
1 code implementation • IJCNLP 2019 • Tao Shen, Xiubo Geng, Tao Qin, Daya Guo, Duyu Tang, Nan Duan, Guodong Long, Daxin Jiang
We consider the problem of conversational question answering over a large-scale knowledge base.
1 code implementation • 15 Sep 2019 • Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Jing Jiang, Michael Blumenstein
In this paper, we propose a medical concept embedding method based on applying a self-attention mechanism to represent each medical concept.
1 code implementation • NeurIPS 2019 • Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
It can significantly improve tasks that suffer from insufficient training data, e. g., few shot learning.
no code implementations • 6 Sep 2019 • Tao Shen, Xiubo Geng, Tao Qin, Guodong Long, Jing Jiang, Daxin Jiang
These two problems lead to a poorly-trained semantic parsing model.
3 code implementations • 15 Jun 2019 • Chun Wang, Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Chengqi Zhang
Graph clustering is a fundamental task which discovers communities or groups in networks.
Ranked #8 on Node Clustering on Cora
8 code implementations • 31 May 2019 • Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Chengqi Zhang
Spatial-temporal graph modeling is an important task to analyze the spatial relations and temporal trends of components in a system.
Ranked #5 on Traffic Prediction on NE-BJ
2 code implementations • 10 May 2019 • Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang
The resulting graph of prototypes can be continually re-used and updated for new tasks and classes.
no code implementations • ICLR 2019 • Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
It addresses the ``many-class'' problem by exploring the class hierarchy, e. g., the coarse-class label that covers a subset of fine classes, which helps to narrow down the candidates for the fine class and is cheaper to obtain.
1 code implementation • 4 Apr 2019 • Fengwen Chen, Shirui Pan, Jing Jiang, Huan Huo, Guodong Long
In this paper, we propose a novel framework called, dual attention graph convolutional networks (DAGCN) to address these problems.
Ranked #25 on Graph Classification on NCI1
no code implementations • 4 Jan 2019 • Shirui Pan, Ruiqi Hu, Sai-fu Fung, Guodong Long, Jing Jiang, Chengqi Zhang
Based on this framework, we derive two variants of adversarial models, the adversarially regularized graph autoencoder (ARGA) and its variational version, adversarially regularized variational graph autoencoder (ARVGA), to learn the graph embedding effectively.
Ranked #7 on Node Clustering on Cora
5 code implementations • 3 Jan 2019 • Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu
In this survey, we provide a comprehensive overview of graph neural networks (GNNs) in data mining and machine learning fields.
4 code implementations • 17 Dec 2018 • Shaoxiong Ji, Shirui Pan, Guodong Long, Xue Li, Jing Jiang, Zi Huang
Federated learning (FL) provides a promising approach to learning private language modeling for intelligent personalized keyboard suggestion by training models in distributed clients rather than training in a central server.
no code implementations • 12 Nov 2018 • Kaixuan Chen, Lina Yao, Dalin Zhang, Xiaojun Chang, Guodong Long, Sen Wang
Semi-supervised learning is crucial for alleviating labelling burdens in people-centric sensing.
no code implementations • 8 May 2018 • Shuai Zhang, Lina Yao, Aixin Sun, Sen Wang, Guodong Long, Manqing Dong
Modeling user-item interaction patterns is an important task for personalized recommendations.
2 code implementations • NAACL 2019 • Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies.
no code implementations • 16 Apr 2018 • Xiang Zhang, Lina Yao, Chaoran Huang, Sen Wang, Mingkui Tan, Guodong Long, Can Wang
Multimodal wearable sensor data classification plays an important role in ubiquitous computing and has a wide range of applications in scenarios from healthcare to entertainment.
1 code implementation • ICLR 2018 • Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Chengqi Zhang
In this paper, we propose a model, called "bi-directional block self-attention network (Bi-BloSAN)", for RNN/CNN-free sequence encoding.
4 code implementations • 13 Feb 2018 • Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics.
Ranked #5 on Link Prediction on Pubmed
1 code implementation • 31 Jan 2018 • Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Sen Wang, Chengqi Zhang
In this paper, we integrate both soft and hard attention into one context fusion model, "reinforced self-attention (ReSA)", for the mutual benefit of each other.
Ranked #56 on Natural Language Inference on SNLI
no code implementations • CIKM '17 Proceedings of the 2017 ACM on Conference on Information and Knowledge Management 2017 • Chun Wang, Shirui Pan, Guodong Long, Xingquan Zhu, Jing Jiang
In this paper, we propose a novel marginalized graph autoencoder (MGAE) algorithm for graph clustering.
3 code implementations • 14 Sep 2017 • Tao Shen, Tianyi Zhou, Guodong Long, Jing Jiang, Shirui Pan, Chengqi Zhang
Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively.
Ranked #68 on Natural Language Inference on SNLI
no code implementations • 14 Jan 2016 • Xiaojun Chang, Yi Yang, Guodong Long, Chengqi Zhang, Alexander G. Hauptmann
In this paper, we focus on automatically detecting events in unconstrained videos without the use of any visual training exemplars.