Search Results for author: Guodong Long

Found 105 papers, 49 papers with code

Personalized Interpretation on Federated Learning: A Virtual Concepts approach

no code implementations28 Jun 2024 Peng Yan, Guodong Long, Jing Jiang, Michael Blumenstein

These conceptual vectors could be pre-defined or refined in a human-in-the-loop process or be learnt via the optimization procedure of the federated learning system.

Federated Learning

Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models

no code implementations24 May 2024 Shengchao Chen, Guodong Long, Jing Jiang, Chengqi Zhang

This paper demonstrates that pre-trained language models (PLMs) are strong foundation models for on-device meteorological variables modeling.


Federated Adaptation for Foundation Model-based Recommendations

1 code implementation8 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.

Federated Learning Privacy Preserving +1

What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning

1 code implementation16 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.

Attribute counterfactual +4

Client-supervised Federated Learning: Towards One-model-for-all Personalization

no code implementations28 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.

Personalized Federated Learning

Dual-Personalizing Adapter for Federated Foundation Models

no code implementations28 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.

Federated Learning

Foundation Models for Weather and Climate Data Understanding: A Comprehensive Survey

1 code implementation5 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.

Thread of Thought Unraveling Chaotic Contexts

no code implementations15 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.

Reading Comprehension

Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution

no code implementations21 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.

reinforcement-learning Reinforcement Learning (RL)

Re-Reading Improves Reasoning in Large Language Models

1 code implementation12 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.


One-Shot Pruning for Fast-adapting Pre-trained Models on Devices

no code implementations10 Jul 2023 Haiyan Zhao, Guodong Long

Large-scale pre-trained models have been remarkably successful in resolving downstream tasks.

Causal Reinforcement Learning: A Survey

no code implementations4 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.

reinforcement-learning Transfer Learning

Personalization Disentanglement for Federated Learning: An explainable perspective

no code implementations6 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.

Disentanglement Personalized Federated Learning

Continual Task Allocation in Meta-Policy Network via Sparse Prompting

1 code implementation29 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.

Federated Prompt Learning for Weather Foundation Models on Devices

no code implementations23 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.

Cloud Computing Federated Learning +2

When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions

no code implementations22 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.

Attribute Federated Learning +1

GPFedRec: Graph-guided Personalization for Federated Recommendation

1 code implementation13 May 2023 Chunxu Zhang, Guodong Long, Tianyi Zhou, Zijjian Zhang, Peng Yan, Bo Yang

The federated recommendation system is an emerging AI service architecture that provides recommendation services in a privacy-preserving manner.

Federated Learning Privacy Preserving +1

Synergistic Interplay between Search and Large Language Models for Information Retrieval

2 code implementations12 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).

Information Retrieval Retrieval

Large Language Models are Strong Zero-Shot Retriever

no code implementations27 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.

Language Modelling Large Language Model +1

Does Continual Learning Equally Forget All Parameters?

no code implementations9 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.

Attribute Continual Learning

A Survey on Deep Learning based Time Series Analysis with Frequency Transformation

no code implementations4 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.

Time Series Time Series Analysis

Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks

no code implementations27 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.

Style-Aware Contrastive Learning for Multi-Style Image Captioning

no code implementations26 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.

Contrastive Learning Image Captioning +1

Multimodal Event Transformer for Image-guided Story Ending Generation

no code implementations26 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.

Decoder Image-guided Story Ending Generation

Improving Cross-modal Alignment for Text-Guided Image Inpainting

no code implementations26 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.

cross-modal alignment Image Inpainting +1

Federated Recommendation with Additive Personalization

1 code implementation22 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.

Federated Learning Recommendation Systems

Prompt Federated Learning for Weather Forecasting: Toward Foundation Models on Meteorological Data

1 code implementation22 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.

Federated Learning Time Series +2

Dual Personalization on Federated Recommendation

1 code implementation16 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.

Privacy Preserving Recommendation Systems

Fine-Grained Distillation for Long Document Retrieval

no code implementations20 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.

Knowledge Distillation Retrieval

Federated Learning on Non-IID Graphs via Structural Knowledge Sharing

1 code implementation23 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.

Federated Learning Graph Learning

CCPrefix: Counterfactual Contrastive Prefix-Tuning for Many-Class Classification

no code implementations11 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.

Classification counterfactual +7

Unsupervised Knowledge Graph Construction and Event-centric Knowledge Infusion for Scientific NLI

no code implementations27 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.

graph construction Natural Language Inference

Federated Learning from Pre-Trained Models: A Contrastive Learning Approach

2 code implementations21 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.

Contrastive Learning Federated Learning

Towards Robust Ranker for Text Retrieval

no code implementations16 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.

Passage Retrieval Text Retrieval

FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels

no code implementations20 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.

Federated Learning Learning with noisy labels

Personalized Federated Learning With Graph

1 code implementation2 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.

Personalized Federated Learning Relation

On the Convergence of Clustered Federated Learning

1 code implementation13 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).

Federated Learning

CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum

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.

Continuous Control reinforcement-learning +1

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

Uncertainty Regularized Policy Learning for Offline Reinforcement Learning

no code implementations29 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.

D4RL Offline RL +2

EAT-C: Environment-Adversarial sub-Task Curriculum for Efficient Reinforcement Learning

no code implementations29 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.

reinforcement-learning Reinforcement Learning (RL)

Vote for Nearest Neighbors Meta-Pruning of Self-Supervised Networks

no code implementations29 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?

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

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.

Attribute Knowledge Graphs +4

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 for Traffic Forecasting

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 traffic data.

Attribute Graph Neural Network

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 Privacy Preserving

Multi-Center Federated Learning: Clients Clustering for Better Personalization

1 code implementation19 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.

Clustering Decision Making +1

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 Graph Neural Network +2

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

1 code implementation20 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.

Graph Embedding Ontology Embedding +1

FedProto: Federated Prototype Learning across Heterogeneous Clients

4 code implementations1 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.

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).

Attribute Generalized Zero-Shot Learning +1

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

no code implementations25 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.

Federated Learning Meta-Learning +3

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 +3

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

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 Relation Extraction +1

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.

Attribute Meta-Learning +1

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 +4

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

2 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 Graph Neural Network +4

Multi-Center Federated Learning: Clients Clustering for Better Personalization

3 code implementations3 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.

Clustering 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.

Graph Embedding Link Prediction +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 +5

Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

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.

General Classification Time Series +2

Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction

no code implementations27 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.

Entity Embeddings Relation +3

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.


Graph WaveNet for Deep Spatial-Temporal Graph Modeling

8 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.

Graph Neural Network Relation +2

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.

Clustering Decoder +4

A Comprehensive Survey on Graph Neural Networks

5 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.

BIG-bench Machine Learning Image Classification +2

Learning Private Neural Language Modeling with Attentive Aggregation

4 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.

Clustering Decoder +3

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.

Hard Attention Natural Language Inference +1

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

3 code implementations14 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 +2

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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