Search Results for author: Guodong Long

Found 58 papers, 29 papers with code

EventBERT: A Pre-Trained Model for Event Correlation Reasoning

no code implementations13 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.

Cloze Test Common Sense Reasoning +1

Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision

no code implementations19 Sep 2021 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.

Knowledge Graphs Relation Extraction +1

Sequential Diagnosis Prediction with Transformer and Ontological Representation

1 code implementation7 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.

Sequential Diagnosis

TraverseNet: Unifying Space and Time in Message Passing

1 code implementation25 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 spatial-temporal graph data.

Federated Learning for Open Banking

no code implementations24 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.

Federated Learning

Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health

no code implementations24 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.

Federated Learning

Multi-Center Federated Learning

1 code implementation19 Aug 2021 Ming Xie, Guodong Long, Tao Shen, Tianyi Zhou, Xianzhi Wang, Jing Jiang, Chengqi Zhang

By comparison, a mixture of multiple global models could capture the heterogeneity across various users if assigning the users to different global models (i. e., centers) in FL.

Federated Learning

Reasoning over Entity-Action-Location Graph for Procedural Text Understanding

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.

graph construction Representation Learning

MIMO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning

1 code implementation20 Jul 2021 Xueping Peng, Guodong Long, Tao Shen, Sen Wang, Zhendong Niu, Chengqi Zhang

To address this, we propose an end-to-end robust Transformer-based solution, Mutual Integration of patient journey and Medical Ontology (MIMO) for healthcare representation learning and predictive analytics.

Graph Embedding

FedProto: Federated Prototype Learning over Heterogeneous Devices

1 code implementation1 May 2021 Yue Tan, Guodong Long, Lu Liu, Tianyi Zhou, Qinghua Lu, Jing Jiang, Chengqi Zhang

The heterogeneity across devices usually hinders the optimization convergence and generalization performance of federated learning (FL) when the aggregation of devices' knowledge occurs in the gradient space.

Federated Learning

Task Aligned Generative Meta-learning for Zero-shot Learning

no code implementations3 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).

Generalized Zero-Shot Learning Meta-Learning

Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning

no code implementations25 Feb 2021 Shaoxiong Ji, Teemu Saravirta, 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.

Federated Learning Meta-Learning +1

Isometric Propagation Network for Generalized Zero-shot Learning

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.

Generalized Zero-Shot Learning

Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task

no code implementations24 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.

Decision Making Hippocampus

MASP: Model-Agnostic Sample Propagation for Few-shot learning

no code implementations1 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.

Few-Shot Learning

Extract Local Inference Chains of Deep Neural Nets

no code implementations1 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.

Interpretable Machine Learning Network Pruning

SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning

no code implementations2 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.

Learning with noisy labels

Confusable Learning for Large-class Few-Shot Classification

no code implementations6 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.

Classification Few-Shot Image Classification +2

Cooperative Heterogeneous Deep Reinforcement Learning

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.

Continuous Control

Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention

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.

Relation Extraction

BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes

1 code implementation24 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.

Attribute Propagation Network for Graph Zero-shot Learning

no code implementations24 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.

Meta-Learning Zero-Shot Learning

Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy

1 code implementation28 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.

Few-Shot Learning

Self-Attention Enhanced Patient Journey Understanding in Healthcare System

1 code implementation15 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.

Interpretable Time-series Classification on Few-shot Samples

1 code implementation3 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.

Classification Few-Shot Learning +3

Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks

3 code implementations24 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.

Graph Learning Multivariate Time Series Forecasting +2

Multi-Center Federated Learning

4 code implementations3 May 2020 Ming Xie, Guodong Long, 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.

Federated Learning

Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion

1 code implementation30 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 #1 on Link Prediction on WN18RR (using extra training data)

Graph Embedding Knowledge Graph Completion +1

Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning

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.

Entity Linking Knowledge Base Completion +4

Rethinking 1D-CNN for Time Series Classification: A Stronger Baseline

2 code implementations24 Feb 2020 Wensi Tang, Guodong Long, Lu Liu, Tianyi Zhou, Jing Jiang, Michael Blumenstein

For time series classification task using 1D-CNN, the selection of kernel size is critically important to ensure the model can capture the right scale salient signal from a long time-series.

Classification General Classification +2

Temporal Self-Attention Network for Medical Concept Embedding

1 code implementation15 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.

Learning to Propagate for Graph Meta-Learning

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.

Few-Shot Image Classification

Graph WaveNet for Deep Spatial-Temporal Graph Modeling

7 code implementations31 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.

Traffic Prediction

MahiNet: A Neural Network for Many-Class Few-Shot Learning with Class Hierarchy

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.

Few-Shot Learning General Classification

DAGCN: Dual Attention Graph Convolutional Networks

1 code implementation4 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.

General Classification Graph Classification +1

Learning Graph Embedding with Adversarial Training Methods

no code implementations4 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.

Graph Clustering Graph Embedding +2

A Comprehensive Survey on Graph Neural Networks

6 code implementations3 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.

Image Classification Natural Language Understanding +1

Learning Private Neural Language Modeling with Attentive Aggregation

3 code implementations17 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.

Federated Learning Language Modelling

NeuRec: On Nonlinear Transformation for Personalized Ranking

no code implementations8 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.

Recommendation Systems

Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together

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.

Multi-modality Sensor Data Classification with Selective Attention

no code implementations16 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.

Classification General Classification

Bi-Directional Block Self-Attention for Fast and Memory-Efficient Sequence Modeling

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.

Adversarially Regularized Graph Autoencoder for Graph Embedding

4 code implementations13 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.

Graph Clustering Graph Embedding +1

Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling

1 code implementation31 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.

Natural Language Inference

DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding

1 code implementation14 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.

Natural Language Inference Sentence Embedding

Dynamic Concept Composition for Zero-Example Event Detection

no code implementations14 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.

Event Detection Zero-Shot Learning

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