Search Results for author: Hui Jiang

Found 50 papers, 16 papers with code

Exploring Dynamic Selection of Branch Expansion Orders for Code Generation

1 code implementation ACL 2021 Hui Jiang, Chulun Zhou, Fandong Meng, Biao Zhang, Jie zhou, Degen Huang, Qingqiang Wu, Jinsong Su

Due to the great potential in facilitating software development, code generation has attracted increasing attention recently.

Code Generation

Reciprocal Feature Learning via Explicit and Implicit Tasks in Scene Text Recognition

1 code implementation13 May 2021 Hui Jiang, Yunlu Xu, Zhanzhan Cheng, ShiLiang Pu, Yi Niu, Wenqi Ren, Fei Wu, Wenming Tan

In this work, we excavate the implicit task, character counting within the traditional text recognition, without additional labor annotation cost.

Scene Text Recognition

Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention Learning

1 code implementation5 Mar 2021 Jinsong Su, Jialong Tang, Hui Jiang, Ziyao Lu, Yubin Ge, Linfeng Song, Deyi Xiong, Le Sun, Jiebo Luo

In aspect-based sentiment analysis (ABSA), many neural models are equipped with an attention mechanism to quantify the contribution of each context word to sentiment prediction.

Aspect-Based Sentiment Analysis

Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters

no code implementations22 Jan 2021 Xianfei Hui, Baiqing Sun, Hui Jiang, Indranil SenGupta

In this paper we implement a combination of data-science and fuzzy theory to improve the classical Barndorff-Nielsen and Shephard model, and implement this to analyze the S&P 500 index.

Time Series

Match$^2$: A Matching over Matching Model for Similar Question Identification

no code implementations21 Jun 2020 Zizhen Wang, Yixing Fan, Jiafeng Guo, Liu Yang, Ruqing Zhang, Yanyan Lan, Xue-Qi Cheng, Hui Jiang, Xiaozhao Wang

However, it has long been a challenge to properly measure the similarity between two questions due to the inherent variation of natural language, i. e., there could be different ways to ask a same question or different questions sharing similar expressions.

Community Question Answering

On Approximation Capabilities of ReLU Activation and Softmax Output Layer in Neural Networks

no code implementations10 Feb 2020 Behnam Asadi, Hui Jiang

In this paper, we have extended the well-established universal approximator theory to neural networks that use the unbounded ReLU activation function and a nonlinear softmax output layer.

General Classification

Dual-FOFE-net Neural Models for Entity Linking with PageRank

no code implementations30 Jul 2019 Feng Wei, Uyen Trang Nguyen, Hui Jiang

Our neural linking models consist of three parts: a PageRank based candidate generation module, a dual-FOFE-net neural ranking model and a simple NIL entity clustering system.

Entity Linking

FreebaseQA: A New Factoid QA Data Set Matching Trivia-Style Question-Answer Pairs with Freebase

1 code implementation NAACL 2019 Kelvin Jiang, Dekun Wu, Hui Jiang

In this paper, we present a new data set, named FreebaseQA, for open-domain factoid question answering (QA) tasks over structured knowledge bases, like Freebase.

Question Answering set matching +1

Bandlimiting Neural Networks Against Adversarial Attacks

no code implementations ICLR 2020 Yuping Lin, Kasra Ahmadi K. A., Hui Jiang

We first explicitly compute the Fourier transform of deep ReLU neural networks and show that there exist decaying but non-zero high frequency components in the Fourier spectrum of neural networks.

Adversarial Attack

Content based News Recommendation via Shortest Entity Distance over Knowledge Graphs

1 code implementation24 May 2019 Kevin Joseph, Hui Jiang

Content-based news recommendation systems need to recommend news articles based on the topics and content of articles without using user specific information.

Knowledge Graphs News Recommendation +1

Effective Context and Fragment Feature Usage for Named Entity Recognition

no code implementations5 Apr 2019 Nargiza Nosirova, MingBin Xu, Hui Jiang

In this paper, we explore a new approach to named entity recognition (NER) with the goal of learning from context and fragment features more effectively, contributing to the improvement of overall recognition performance.

named-entity-recognition NER +1

Why Learning of Large-Scale Neural Networks Behaves Like Convex Optimization

no code implementations6 Mar 2019 Hui Jiang

Furthermore, we have proved that gradient descent methods surely converge to a global minimum of zero loss provided that the disparity matrices maintain full rank.

Fixed-Size Ordinally Forgetting Encoding Based Word Sense Disambiguation

no code implementations23 Feb 2019 Xi Zhu, MingBin Xu, Hui Jiang

In this paper, we present our method of using fixed-size ordinally forgetting encoding (FOFE) to solve the word sense disambiguation (WSD) problem.

Language Modelling Word Sense Disambiguation

A New Perspective on Machine Learning: How to do Perfect Supervised Learning

no code implementations7 Jan 2019 Hui Jiang

In this work, we introduce the concept of bandlimiting into the theory of machine learning because all physical processes are bandlimited by nature, including real-world machine learning tasks.

Generalization Bounds

DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks

no code implementations16 Nov 2018 Hengyue Pan, Hui Jiang, Xin Niu, Yong Dou

Most of previous methods mainly consider to drop features from input data and hidden layers, such as Dropout, Cutout and DropBlocks.

Image Classification

Dual Fixed-Size Ordinally Forgetting Encoding (FOFE) for Competitive Neural Language Models

no code implementations EMNLP 2018 Sedtawut Watcharawittayakul, MingBin Xu, Hui Jiang

In this paper, we propose a new approach to employ the fixed-size ordinally-forgetting encoding (FOFE) (Zhang et al., 2015b) in neural languages modelling, called dual-FOFE.

Machine Translation Speech Recognition +1

Explicit Utilization of General Knowledge in Machine Reading Comprehension

no code implementations ACL 2019 Chao Wang, Hui Jiang

To bridge the gap between Machine Reading Comprehension (MRC) models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, in this paper, we explore how to integrate the neural networks of MRC models with the general knowledge of human beings.

Machine Reading Comprehension Question Answering

The Lower The Simpler: Simplifying Hierarchical Recurrent Models

no code implementations NAACL 2019 Chao Wang, Hui Jiang

To improve the training efficiency of hierarchical recurrent models without compromising their performance, we propose a strategy named as `the lower the simpler', which is to simplify the baseline models by making the lower layers simpler than the upper layers.

Using Neural Network for Identifying Clickbaits in Online News Media

1 code implementation20 Jun 2018 Amin Omidvar, Hui Jiang, Aijun An

Online news media sometimes use misleading headlines to lure users to open the news article.

Clickbait Detection

Accurate and Efficient Estimation of Small P-values with the Cross-Entropy Method: Applications in Genomic Data Analysis

1 code implementation9 Mar 2018 Yang Shi, Mengqiao Wang, Weiping Shi, Ji-Hyun Lee, Huining Kang, Hui Jiang

Small $p$-values are often required to be accurately estimated in large scale genomic studies for the adjustment of multiple hypothesis tests and the ranking of genomic features based on their statistical significance.


Word Embeddings based on Fixed-Size Ordinally Forgetting Encoding

no code implementations EMNLP 2017 Joseph Sanu, MingBin Xu, Hui Jiang, Quan Liu

In this paper, we propose to learn word embeddings based on the recent fixed-size ordinally forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence into a fixed-size representation.

Language Modelling Semantic Textual Similarity +2

Recurrent Neural Network-Based Sentence Encoder with Gated Attention for Natural Language Inference

2 code implementations WS 2017 Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, Diana Inkpen

The RepEval 2017 Shared Task aims to evaluate natural language understanding models for sentence representation, in which a sentence is represented as a fixed-length vector with neural networks and the quality of the representation is tested with a natural language inference task.

Natural Language Inference Natural Language Understanding

P-splines with an $\ell_1$ penalty for repeated measures

1 code implementation27 Jul 2017 Brian D. Segal, Michael R. Elliott, Thomas Braun, Hui Jiang

In addition to $\ell_2$ penalties, $\ell_1$-type penalties have been used in nonparametric and semiparametric regression to achieve greater flexibility, such as in locally adaptive regression splines, $\ell_1$ trend filtering, and the fused lasso additive model.

Methodology 62G08 (Primary), 62P10 (Secondary)

A Local Detection Approach for Named Entity Recognition and Mention Detection

no code implementations ACL 2017 Mingbin Xu, Hui Jiang, Sedtawut Watcharawittayakul

In this paper, we study a novel approach for named entity recognition (NER) and mention detection (MD) in natural language processing.

Feature Engineering Image Classification +4

Supervised Adversarial Networks for Image Saliency Detection

no code implementations24 Apr 2017 Hengyue Pan, Hui Jiang

In the past few years, Generative Adversarial Network (GAN) became a prevalent research topic.

Image Generation Saliency Detection

Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering

no code implementations14 Mar 2017 Junbei Zhang, Xiaodan Zhu, Qian Chen, Li-Rong Dai, Si Wei, Hui Jiang

The last several years have seen intensive interest in exploring neural-network-based models for machine comprehension (MC) and question answering (QA).

Question Answering Reading Comprehension

Commonsense Knowledge Enhanced Embeddings for Solving Pronoun Disambiguation Problems in Winograd Schema Challenge

no code implementations13 Nov 2016 Quan Liu, Hui Jiang, Zhen-Hua Ling, Xiaodan Zhu, Si Wei, Yu Hu

The PDP task we investigate in this paper is a complex coreference resolution task which requires the utilization of commonsense knowledge.

Coreference Resolution

Neural Networks Models for Entity Discovery and Linking

no code implementations11 Nov 2016 Dan Liu, Wei. Lin, Shiliang Zhang, Si Wei, Hui Jiang

This paper describes the USTC_NELSLIP systems submitted to the Trilingual Entity Detection and Linking (EDL) track in 2016 TAC Knowledge Base Population (KBP) contests.

Entity Linking Knowledge Base Population

A FOFE-based Local Detection Approach for Named Entity Recognition and Mention Detection

1 code implementation2 Nov 2016 Mingbin Xu, Hui Jiang

In this paper, we study a novel approach for named entity recognition (NER) and mention detection in natural language processing.

named-entity-recognition Natural Language Processing +1

Distraction-Based Neural Networks for Document Summarization

1 code implementation26 Oct 2016 Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang

Distributed representation learned with neural networks has recently shown to be effective in modeling natural languages at fine granularities such as words, phrases, and even sentences.

Document Summarization

Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and Estimation

1 code implementation20 Jun 2016 Hengyue Pan, Hui Jiang

Convolutional neural networks (CNNs) have yielded the excellent performance in a variety of computer vision tasks, where CNNs typically adopt a similar structure consisting of convolution layers, pooling layers and fully connected layers.

Image Augmentation Image Classification

Higher Order Recurrent Neural Networks

no code implementations30 Apr 2016 Rohollah Soltani, Hui Jiang

In this paper, we study novel neural network structures to better model long term dependency in sequential data.

Language Modelling

Part-of-Speech Relevance Weights for Learning Word Embeddings

no code implementations24 Mar 2016 Quan Liu, Zhen-Hua Ling, Hui Jiang, Yu Hu

The model proposed in this paper paper jointly optimizes word vectors and the POS relevance matrices.

Learning Word Embeddings POS +1

Generating images with recurrent adversarial networks

1 code implementation16 Feb 2016 Daniel Jiwoong Im, Chris Dongjoo Kim, Hui Jiang, Roland Memisevic

Gatys et al. (2015) showed that optimizing pixels to match features in a convolutional network with respect reference image features is a way to render images of high visual quality.

A Deep Learning Based Fast Image Saliency Detection Algorithm

no code implementations1 Feb 2016 Hengyue Pan, Hui Jiang

In this paper, we propose a fast deep learning method for object saliency detection using convolutional neural networks.

Saliency Detection Superpixels

Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency

no code implementations28 Dec 2015 Shiliang Zhang, Cong Liu, Hui Jiang, Si Wei, Li-Rong Dai, Yu Hu

In this paper, we propose a novel neural network structure, namely \emph{feedforward sequential memory networks (FSMN)}, to model long-term dependency in time series without using recurrent feedback.

Speech Recognition Time Series

Feedforward Sequential Memory Neural Networks without Recurrent Feedback

no code implementations9 Oct 2015 ShiLiang Zhang, Hui Jiang, Si Wei, Li-Rong Dai

We introduce a new structure for memory neural networks, called feedforward sequential memory networks (FSMN), which can learn long-term dependency without using recurrent feedback.

Language Modelling

A Fixed-Size Encoding Method for Variable-Length Sequences with its Application to Neural Network Language Models

1 code implementation6 May 2015 Shiliang Zhang, Hui Jiang, MingBin Xu, JunFeng Hou, Li-Rong Dai

In this paper, we propose the new fixed-size ordinally-forgetting encoding (FOFE) method, which can almost uniquely encode any variable-length sequence of words into a fixed-size representation.

Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks

no code implementations3 Feb 2015 Shiliang Zhang, Hui Jiang

As a result, the HOPE framework can be used as a novel tool to probe why and how NNs work, more importantly, to learn NNs in either supervised or unsupervised ways.

Image Classification Speech Recognition

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