Search Results for author: Fang Wang

Found 22 papers, 6 papers with code

Training Entire-Space Models for Target-oriented Opinion Words Extraction

1 code implementation15 Apr 2022 Yuncong Li, Fang Wang, Sheng-hua Zhong

Moreover, the performance of these models on the first type of instance cannot reflect their performance on entire space.

Aspect-Based Sentiment Analysis Selection bias +1

EIT: Efficiently Lead Inductive Biases to ViT

no code implementations14 Mar 2022 Rui Xia, JingChao Wang, Chao Xue, Boyu Deng, Fang Wang

In four popular small-scale datasets, compared with ViT, EIT has an accuracy improvement of 12. 6% on average with fewer parameters and FLOPs.

Aspect-Sentiment-Multiple-Opinion Triplet Extraction

1 code implementation14 Oct 2021 Fang Wang, Yuncong Li, Sheng-hua Zhong, Cunxiang Yin, Yancheng He

Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term (aspect), sentiment and opinion term (opinion) triplets from sentences and can tell a complete story, i. e., the discussed aspect, the sentiment toward the aspect, and the cause of the sentiment.

Aspect Sentiment Triplet Extraction Extract Aspect

Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans

no code implementations24 Sep 2021 Tai-Hsien Wu, Chunfeng Lian, Sanghee Lee, Matthew Pastewait, Christian Piers, Jie Liu, Fang Wang, Li Wang, Chiung-Ying Chiu, Wenchi Wang, Christina Jackson, Wei-Lun Chao, Dinggang Shen, Ching-Chang Ko

Our TS-MDL first adopts an end-to-end \emph{i}MeshSegNet method (i. e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan.

Code Generation

Investigation of Bare-bones Algorithms from Quantum Perspective: A Quantum Dynamical Global Optimizer

no code implementations26 Jun 2021 Peng Wang, Gang Xin, Fang Wang

Correspondingly, the basic search behaviour is derived, which constitutes the basic iterative process of a simple optimization system.

Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors

no code implementations20 May 2021 Jingyi Zhang, Huolan Zhu, Yongkai Chen, Chenguang Yang, Huimin Cheng, Yi Li, Wenxuan Zhong, Fang Wang

Background: Extensive clinical evidence suggests that a preventive screening of coronary heart disease (CHD) at an earlier stage can greatly reduce the mortality rate.

Classification

A More Fine-Grained Aspect-Sentiment-Opinion Triplet Extraction Task

5 code implementations29 Mar 2021 Yuncong Li, Fang Wang, Wenjun Zhang, Sheng-hua Zhong, Cunxiang Yin, Yancheng He

Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term, sentiment and opinion term triplets from sentences and tries to provide a complete solution for aspect-based sentiment analysis (ABSA).

Aspect-Sentiment-Opinion Triplet Extraction

K-XLNet: A General Method for Combining Explicit Knowledge with Language Model Pretraining

no code implementations25 Mar 2021 Ruiqing Yan, Lanchang Sun, Fang Wang, XiaoMing Zhang

Though pre-trained language models such as Bert and XLNet, have rapidly advanced the state-of-the-art on many NLP tasks, they implicit semantics only relying on surface information between words in corpus.

Common Sense Reasoning Language Modelling

LightMBERT: A Simple Yet Effective Method for Multilingual BERT Distillation

no code implementations11 Mar 2021 Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, Qun Liu

The multilingual pre-trained language models (e. g, mBERT, XLM and XLM-R) have shown impressive performance on cross-lingual natural language understanding tasks.

Natural Language Understanding

Improving Task-Agnostic BERT Distillation with Layer Mapping Search

no code implementations11 Dec 2020 Xiaoqi Jiao, Huating Chang, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, Qun Liu

Comprehensive experiments on the evaluation benchmarks demonstrate that 1) layer mapping strategy has a significant effect on task-agnostic BERT distillation and different layer mappings can result in quite different performances; 2) the optimal layer mapping strategy from the proposed search process consistently outperforms the other heuristic ones; 3) with the optimal layer mapping, our student model achieves state-of-the-art performance on the GLUE tasks.

Knowledge Distillation

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-resolved EEG Motor Imagery Signals

1 code implementation16 Jun 2020 Yimin Hou, Shuyue Jia, Xiangmin Lun, Shu Zhang, Tao Chen, Fang Wang, Jinglei Lv

To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain motor imagery.

EEG

Deep Feature Mining via Attention-based BiLSTM-GCN for Human Motor Imagery Recognition

no code implementations2 May 2020 Yimin Hou, Shuyue Jia, Xiangmin Lun, Shu Zhang, Tao Chen, Fang Wang, Jinglei Lv

The introduced deep feature mining approach can precisely recognize human motion intents from raw EEG signals, which paves the road to translate the EEG based MI recognition to practical BCI systems.

EEG

TinyBERT: Distilling BERT for Natural Language Understanding

5 code implementations Findings of the Association for Computational Linguistics 2020 Xiaoqi Jiao, Yichun Yin, Lifeng Shang, Xin Jiang, Xiao Chen, Linlin Li, Fang Wang, Qun Liu

To accelerate inference and reduce model size while maintaining accuracy, we first propose a novel Transformer distillation method that is specially designed for knowledge distillation (KD) of the Transformer-based models.

Knowledge Distillation Language Modelling +6

Convolutional Neural Network for Universal Sentence Embeddings

no code implementations COLING 2018 Xiaoqi Jiao, Fang Wang, Dan Feng

This paper proposes a simple CNN model for creating general-purpose sentence embeddings that can transfer easily across domains and can also act as effective initialization for downstream tasks.

Semantic Textual Similarity Sentence Embeddings +2

Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach

no code implementations3 Jun 2017 Hu Han, Anil K. Jain, Fang Wang, Shiguang Shan, Xilin Chen

In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes.

Multi-Task Learning Representation Learning

Prediction of Manipulation Actions

no code implementations3 Oct 2016 Cornelia Fermüller, Fang Wang, Yezhou Yang, Konstantinos Zampogiannis, Yi Zhang, Francisco Barranco, Michael Pfeiffer

In psychophysical experiments, we evaluated human observers' skills in predicting actions from video sequences of different length, depicting the hand movement in the preparation and execution of actions before and after contact with the object.

Compositional Memory for Visual Question Answering

no code implementations18 Nov 2015 Aiwen Jiang, Fang Wang, Fatih Porikli, Yi Li

We then feed the episodes to a standard question answering module together with the contextual visual information and linguistic information.

Question Answering Visual Question Answering +1

Sketch-based 3D Shape Retrieval using Convolutional Neural Networks

no code implementations CVPR 2015 Fang Wang, Le Kang, Yi Li

Almost always in state of the art approaches a large amount of "best views" are computed for 3D models, with the hope that the query sketch matches one of these 2D projections of 3D models using predefined features.

3D Shape Classification 3D Shape Retrieval +1

Beyond Physical Connections: Tree Models in Human Pose Estimation

no code implementations CVPR 2013 Fang Wang, Yi Li

Our method outperformed the state of the art on the LSP, both in the scenarios when the training images are from the same dataset and from the PARSE dataset.

Pose Estimation

Learning Visual Symbols for Parsing Human Poses in Images

no code implementations23 Apr 2013 Fang Wang, Yi Li

When the structure of the compositional parts is a tree, we derive an efficient approach to estimating human poses in images.

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