b'Guodong Long'

research

∙ 09/12/2023

Re-Reading Improves Reasoning in Language Models

Reasoning presents a significant and challenging issue for Large Languag...

0 Xiaohan Xu, et al. ∙

research

∙ 07/10/2023

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

Large-scale pre-trained models have been remarkably successful in resolv...

0 Haiyan Zhao, et al. ∙

research

∙ 07/04/2023

Causal Reinforcement Learning: A Survey

Reinforcement learning is an essential paradigm for solving sequential d...

0 Zhihong Deng, et al. ∙

research

∙ 06/06/2023

Personalization Disentanglement for Federated Learning

Personalized federated learning (PFL) jointly trains a variety of local ...

0 Peng Yan, et al. ∙

research

∙ 06/01/2023

Improving the Robustness of Summarization Systems with Dual Augmentation

A robust summarization system should be able to capture the gist of the ...

0 Xiuying Chen, et al. ∙

research

∙ 05/29/2023

Continual Task Allocation in Meta-Policy Network via Sparse Prompting

How to train a generalizable meta-policy by continually learning a seque...

0 Yijun Yang, et al. ∙

research

∙ 05/23/2023

Spatial-temporal Prompt Learning for Federated Weather Forecasting

Federated weather forecasting is a promising collaborative learning fram...

0 Shengchao Chen, et al. ∙

research

∙ 05/12/2023

Knowledge Refinement via Interaction Between Search Engines and Large Language Models

Information retrieval (IR) plays a crucial role in locating relevant res...

0 Jiazhan Feng, et al. ∙

research

∙ 04/27/2023

Large Language Models are Strong Zero-Shot Retriever

In this work, we propose a simple method that applies a large language m...

0 Tao Shen, et al. ∙

research

∙ 04/09/2023

Does Continual Learning Equally Forget All Parameters?

Distribution shift (e.g., task or domain shift) in continual learning (C...

0 Haiyan Zhao, et al. ∙

research

∙ 02/04/2023

Neural Time Series Analysis with Fourier Transform: A Survey

Recently, Fourier transform has been widely introduced into deep neural ...

0 Kun Yi, et al. ∙

research

∙ 01/27/2023

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

As a few large-scale pre-trained models become the major choices of vari...

0 Haiyan Zhao, et al. ∙

research

∙ 01/26/2023

Style-Aware Contrastive Learning for Multi-Style Image Captioning

Existing multi-style image captioning methods show promising results in ...

0 Yucheng Zhou, et al. ∙

research

∙ 01/26/2023

Improving Cross-modal Alignment for Text-Guided Image Inpainting

Text-guided image inpainting (TGII) aims to restore missing regions base...

0 Yucheng Zhou, et al. ∙

research

∙ 01/26/2023

Multimodal Event Transformer for Image-guided Story Ending Generation

Image-guided story ending generation (IgSEG) is to generate a story endi...

0 Yucheng Zhou, et al. ∙

research

∙ 01/22/2023

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

To tackle the global climate challenge, it urgently needs to develop a c...

0 Shengchao Chen, et al. ∙

research

∙ 01/22/2023

Federated Recommendation with Additive Personalization

With rising concerns about privacy, developing recommendation systems in...

0 Zhiwei Li, et al. ∙

research

∙ 12/20/2022

Fine-Grained Distillation for Long Document Retrieval

Long document retrieval aims to fetch query-relevant documents from a la...

0 Yucheng Zhou, et al. ∙

research

∙ 11/23/2022

Federated Learning on Non-IID Graphs via Structural Knowledge Sharing

Graph neural networks (GNNs) have shown their superiority in modeling gr...

1 Yue Tan, et al. ∙

research

∙ 10/27/2022

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

With the advance of natural language inference (NLI), a rising demand fo...

0 Chenglin Wang, et al. ∙

research

∙ 09/21/2022

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

Federated Learning (FL) is a machine learning paradigm that allows decen...

0 Yue Tan, et al. ∙

research

∙ 06/16/2022

Towards Robust Ranker for Text Retrieval

A ranker plays an indispensable role in the de facto 'retrieval rera...

0 Yucheng Zhou, et al. ∙

research

∙ 05/20/2022

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

Federated learning (FL) aims at training a global model on the server si...

0 Zhuowei Wang, et al. ∙

research

∙ 03/04/2022

ClarET: Pre-training a Correlation-Aware Context-To-Event Transformer for Event-Centric Generation and Classification

Generating new events given context with correlated ones plays a crucial...

0 Yucheng Zhou, et al. ∙

research

∙ 02/13/2022

On the Convergence of Clustered Federated Learning

In a federated learning system, the clients, e.g. mobile devices and org...

0 Jie Ma, et al. ∙

research

∙ 10/13/2021

EventBERT: A Pre-Trained Model for Event Correlation Reasoning

Event correlation reasoning infers whether a natural language paragraph ...

0 Yucheng Zhou, et al. ∙

research

∙ 09/07/2021

Sequential Diagnosis Prediction with Transformer and Ontological Representation

Sequential diagnosis prediction on the Electronic Health Record (EHR) ha...

0 Xueping Peng, et al. ∙

research

∙ 09/06/2021

Eliminating Sentiment Bias for Aspect-Level Sentiment Classification with Unsupervised Opinion Extraction

Aspect-level sentiment classification (ALSC) aims at identifying the sen...

0 Bo Wang, et al. ∙

research

∙ 08/24/2021

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

Privacy protection is an ethical issue with broad concern in Artificial ...

0 Guodong Long, et al. ∙

research

∙ 08/24/2021

Federated Learning for Open Banking

Open banking enables individual customers to own their banking data, whi...

0 Guodong Long, et al. ∙

research

∙ 07/20/2021

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

Healthcare representation learning on the Electronic Health Record (EHR)...

0 Xueping Peng, et al. ∙

research

∙ 07/10/2021

Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering

Graph convolutional networks are becoming indispensable for deep learnin...

0 Zonghan Wu, et al. ∙

research

∙ 05/01/2021

FedProto: Federated Prototype Learning over Heterogeneous Devices

The heterogeneity across devices usually hinders the optimization conver...

0 Yue Tan, et al. ∙

research

∙ 03/03/2021

Task Aligned Generative Meta-learning for Zero-shot Learning

Zero-shot learning (ZSL) refers to the problem of learning to classify i...

0 Zhe Liu, et al. ∙

research

∙ 02/25/2021

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

Federated learning is a new learning paradigm that decouples data collec...

17 Shaoxiong Ji, et al. ∙

research

∙ 02/03/2021

Isometric Propagation Network for Generalized Zero-shot Learning

Zero-shot learning (ZSL) aims to classify images of an unseen class only...

6 Lu Liu, et al. ∙

research

∙ 12/02/2020

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

Deep learning with noisy labels is a challenging task. Recent prominent ...

0 Zhuowei Wang, et al. ∙

research

∙ 11/06/2020

Confusable Learning for Large-class Few-Shot Classification

Few-shot image classification is challenging due to the lack of ample sa...

0 Bingcong Li, et al. ∙

research

∙ 11/02/2020

Cooperative Heterogeneous Deep Reinforcement Learning

Numerous deep reinforcement learning agents have been proposed, and each...

0 Han Zheng, et al. ∙

research

∙ 10/10/2020

RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion

Many graph embedding approaches have been proposed for knowledge graph c...

0 Hao Huang, et al. ∙

research

∙ 09/24/2020

Attribute Propagation Network for Graph Zero-shot Learning

The goal of zero-shot learning (ZSL) is to train a model to classify sam...

10 Lu Liu, et al. ∙

research

∙ 09/24/2020

BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes

Electronic health records (EHRs) are longitudinal records of a patient's...

0 Xueping Peng, et al. ∙

research

∙ 06/28/2020

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

We study many-class few-shot (MCFS) problem in both supervised learning ...

5 Lu Liu, et al. ∙

research

∙ 06/21/2020

A Universal Representation Transformer Layer for Few-Shot Image Classification

Few-shot classification aims to recognize unseen classes when presented ...

0 Lu Liu, et al. ∙

research

∙ 06/03/2020

Interpretable Time-series Classification on Few-shot Samples

Recent few-shot learning works focus on training a model with prior meta...

29 Wensi Tang, et al. ∙

research

∙ 05/24/2020

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

Modeling multivariate time series has long been a subject that has attra...

0 Zonghan Wu, et al. ∙

research

∙ 05/03/2020

Multi-Center Federated Learning

Federated learning has received great attention for its capability to tr...

10 Ming Xie, et al. ∙

research

∙ 04/30/2020

Semantic Triple Encoder for Fast Open-Set Link Prediction

We improve both the open-set generalization and efficiency of link predi...

0 Bo Wang, et al. ∙

research

∙ 04/29/2020

Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning

In this work, we aim at equipping pre-trained language models with struc...

0 Tao Shen, et al. ∙

research

∙ 02/24/2020

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

For time series classification task using 1D-CNN, the selection of kerne...

3 Wensi Tang, et al. ∙

Guodong Long

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