Search Results for author: Fang Fang

Found 27 papers, 5 papers with code

CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction

no code implementations COLING 2022 Yubing Ren, Yanan Cao, Fang Fang, Ping Guo, Zheng Lin, Wei Ma, Yi Liu

Transforming the large amounts of unstructured text on the Internet into structured event knowledge is a critical, yet unsolved goal of NLP, especially when addressing document-level text.

Document-level Event Extraction Event Extraction

Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network

no code implementations EMNLP 2020 Ruipeng Jia, Yanan Cao, Hengzhu Tang, Fang Fang, Cong Cao, Shi Wang

Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations.

Extractive Summarization Extractive Text Summarization +2

TEBNER: Domain Specific Named Entity Recognition with Type Expanded Boundary-aware Network

no code implementations EMNLP 2021 Zheng Fang, Yanan Cao, Tai Li, Ruipeng Jia, Fang Fang, Yanmin Shang, Yuhai Lu

To alleviate label scarcity in Named Entity Recognition (NER) task, distantly supervised NER methods are widely applied to automatically label data and identify entities.

named-entity-recognition Named Entity Recognition +1

Fast calculation of Counterparty Credit exposures and associated sensitivities using fourier series expansion

no code implementations21 Nov 2023 Gijs Mast, Xiaoyu Shen, Fang Fang

This paper introduces a novel approach for computing netting--set level and counterparty level exposures, such as Potential Future Exposure (PFE) and Expected Exposure (EE), along with associated sensitivities.

Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learning

no code implementations3 Nov 2023 Bibo Wu, Fang Fang, Xianbin Wang, Donghong Cai, Shu Fu, Zhiguo Ding

Subsequently, given the fuzzy based client-edge association, a joint edge server scheduling and resource allocation problem is formulated.

Federated Learning Problem Decomposition +1

MIM-GAN-based Anomaly Detection for Multivariate Time Series Data

1 code implementation26 Oct 2023 Shan Lu, Zhicheng Dong, Donghong Cai, Fang Fang, Dongcai Zhao

To avoid the local optimal solution of loss function and the model collapse, we introduce an exponential information measure into the loss function of GAN.

Anomaly Detection Diversity +3

Towards Better Entity Linking with Multi-View Enhanced Distillation

1 code implementation27 May 2023 Yi Liu, Yuan Tian, Jianxun Lian, Xinlong Wang, Yanan Cao, Fang Fang, Wen Zhang, Haizhen Huang, Denvy Deng, Qi Zhang

Aiming at learning entity representations that can match divergent mentions, this paper proposes a Multi-View Enhanced Distillation (MVD) framework, which can effectively transfer knowledge of multiple fine-grained and mention-relevant parts within entities from cross-encoders to dual-encoders.

Entity Linking Knowledge Distillation +1

Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networks

no code implementations18 Apr 2023 Bibo Wu, Fang Fang, Xianbin Wang

To address these challenges, in this paper, a joint optimization problem of client selection and resource allocation is formulated, aiming to minimize the total time consumption of each round in FL over a non-orthogonal multiple access (NOMA) enabled wireless network.

Federated Learning

Time-aware Multiway Adaptive Fusion Network for Temporal Knowledge Graph Question Answering

no code implementations24 Feb 2023 Yonghao Liu, Di Liang, Fang Fang, Sirui Wang, Wei Wu, Rui Jiang

For each given question, TMA first extracts the relevant concepts from the KG, and then feeds them into a multiway adaptive module to produce a \emph{temporal-specific} representation of the question.

Graph Question Answering Knowledge Graphs +1

AI of Brain and Cognitive Sciences: From the Perspective of First Principles

no code implementations20 Jan 2023 Luyao Chen, Zhiqiang Chen, Longsheng Jiang, Xiang Liu, Linlu Xu, Bo Zhang, Xiaolong Zou, Jinying Gao, Yu Zhu, Xizi Gong, Shan Yu, Sen Song, Liangyi Chen, Fang Fang, Si Wu, Jia Liu

Nowadays, we have witnessed the great success of AI in various applications, including image classification, game playing, protein structure analysis, language translation, and content generation.

Few-Shot Learning Image Classification

Joint Robust Beamforming Design for WPT-assisted D2D Communications in MISO-NOMA: Fractional Programming and Deep Reinforcement Learning

no code implementations25 Sep 2022 Shiyu Jiao, Fang Fang, Zhiguo Ding

The proposed PFP algorithm and the DDPG-based algorithm are compared in the presence of different channel estimation errors.

FragmGAN: Generative Adversarial Nets for Fragmentary Data Imputation and Prediction

no code implementations9 Mar 2022 Fang Fang, Shenliao Bao

Modern scientific research and applications very often encounter "fragmentary data" which brings big challenges to imputation and prediction.

Imputation

Optimal Model Averaging of Support Vector Machines in Diverging Model Spaces

no code implementations24 Dec 2021 Chaoxia Yuan, Chao Ying, Zhou Yu, Fang Fang

Support vector machine (SVM) is a powerful classification method that has achieved great success in many fields.

Model Selection

Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning

no code implementations30 Nov 2021 Yi Guo, Fang Fang, Donghong Cai, Zhiguo Ding

Simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) has been considered as a promising auxiliary device to enhance the performance of the wireless network, where users located at the different sides of the surfaces can be simultaneously served by the transmitting and reflecting signals.

Reinforcement Learning (RL)

SL-CycleGAN: Blind Motion Deblurring in Cycles using Sparse Learning

1 code implementation7 Nov 2021 Ali Syed Saqlain, Li-Yun Wang, Fang Fang

In this paper, we introduce an end-to-end generative adversarial network (GAN) based on sparse learning for single image blind motion deblurring, which we called SL-CycleGAN.

Deblurring Generative Adversarial Network +3

Is There More Pattern in Knowledge Graph? Exploring Proximity Pattern for Knowledge Graph Embedding

no code implementations2 Oct 2021 Ren Li, Yanan Cao, Qiannan Zhu, Xiaoxue Li, Fang Fang

Modeling of relation pattern is the core focus of previous Knowledge Graph Embedding works, which represents how one entity is related to another semantically by some explicit relation.

Knowledge Graph Completion Knowledge Graph Embedding +1

How Does Knowledge Graph Embedding Extrapolate to Unseen Data: A Semantic Evidence View

1 code implementation24 Sep 2021 Ren Li, Yanan Cao, Qiannan Zhu, Guanqun Bi, Fang Fang, Yi Liu, Qian Li

However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate.

Graph Neural Network Knowledge Graph Completion +2

Deep Differential Amplifier for Extractive Summarization

no code implementations ACL 2021 Ruipeng Jia, Yanan Cao, Fang Fang, Yuchen Zhou, Zheng Fang, Yanbing Liu, Shi Wang

In this paper, we conceptualize the single-document extractive summarization as a rebalance problem and present a deep differential amplifier framework.

Extractive Summarization imbalanced classification +1

Which Invariance Should We Transfer? A Causal Minimax Learning Approach

1 code implementation5 Jul 2021 Mingzhou Liu, Xiangyu Zheng, Xinwei Sun, Fang Fang, Yizhou Wang

When this condition fails, we surprisingly find with an example that this whole stable set, although can fully exploit stable information, is not the optimal one to transfer.

Domain Generalization

What Role Do Intelligent Reflecting Surfaces Play in Multi-Antenna Non-Orthogonal Multiple Access?

no code implementations20 Sep 2020 Arthur S. de Sena, Dick Carrillo, Fang Fang, Pedro H. J. Nardelli, Daniel B. da Costa, Ugo S. Dias, Zhiguo Ding, Constantinos B. Papadias, Walid Saad

Massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) are two key techniques for enabling massive connectivity in future wireless networks.

Fairness

Energy-Efficient Resource Allocation for NOMA enabled MEC Networks with Imperfect CSI

no code implementations14 Sep 2020 Fang Fang, Kaidi Wang, Zhiguo Ding, Victor C. M. Leung

In this paper, we mainly focus on energy-efficient resource allocation for a multi-user, multi-BS NOMA assisted MEC network with imperfect channel state information (CSI), in which each user can upload its tasks to multiple base stations (BSs) for remote executions.

Edge-computing

Joint Optimization of Beamforming, Phase-Shifting and Power Allocation in a Multi-cluster IRS-NOMA Network

no code implementations14 Sep 2020 Ximing Xie, Fang Fang, Zhiguo Ding

To address this non-convex problem, we propose an alternating optimization based algorithm.

Optimal Resource Allocation for Delay Minimization in NOMA-MEC Networks

no code implementations11 Sep 2020 Fang Fang, Yanqing Xu, Zhiguo Ding, Chao Shen, Mugen Peng, George K. Karagiannidis

We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts.

Edge-computing

Energy-Efficient Design of IRS-NOMA Networks

no code implementations11 Sep 2020 Fang Fang, Yanqing Xu, Quoc-Viet Pham, Zhiguo Ding

Combining intelligent reflecting surface (IRS) and non-orthogonal multiple access (NOMA) is an effective solution to enhance communication coverage and energy efficiency.

HIN: Hierarchical Inference Network for Document-Level Relation Extraction

no code implementations28 Mar 2020 Hengzhu Tang, Yanan Cao, Zhen-Yu Zhang, Jiangxia Cao, Fang Fang, Shi Wang, Pengfei Yin

In this paper, we propose a Hierarchical Inference Network (HIN) to make full use of the abundant information from entity level, sentence level and document level.

Document-level Relation Extraction Relation +2

A Novel Method to Study Bottom-up Visual Saliency and its Neural Mechanism

no code implementations13 Apr 2016 Cheng Chen, Xilin Zhang, Yizhou Wang, Fang Fang

In this study, we propose a novel method to measure bottom-up saliency maps of natural images.

6th International Symposium on Attention in Cognitive Systems 2013

no code implementations22 Jul 2013 Lucas Paletta, Laurent Itti, Björn Schuller, Fang Fang

This volume contains the papers accepted at the 6th International Symposium on Attention in Cognitive Systems (ISACS 2013), held in Beijing, August 5, 2013.

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