Search Results for author: Fang Fang

Found 17 papers, 2 papers with code

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 NER

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 +1

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

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 Image Deblurring +2

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

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.

Knowledge Graph Completion Knowledge Graph Embedding +1

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

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

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.

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

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.

Relation Extraction Translation

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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