b'Hui Jiang'

research

∙ 07/14/2023

FedBIAD: Communication-Efficient and Accuracy-Guaranteed Federated Learning with Bayesian Inference-Based Adaptive Dropout

Federated Learning (FL) emerges as a distributed machine learning paradi...

0 Jingjing Xue, et al. ∙

research

∙ 04/19/2023

A Latent Space Theory for Emergent Abilities in Large Language Models

Languages are not created randomly but rather to communicate information...

0 Hui Jiang, et al. ∙

research

∙ 04/12/2023

FedTrip: A Resource-Efficient Federated Learning Method with Triplet Regularization

In the federated learning scenario, geographically distributed clients c...

0 Xujing Li, et al. ∙

research

∙ 10/17/2022

Towards Robust k-Nearest-Neighbor Machine Translation

k-Nearest-Neighbor Machine Translation (kNN-MT) becomes an important res...

0 Hui Jiang, et al. ∙

research

∙ 06/01/2021

Exploring Dynamic Selection of Branch Expansion Orders for Code Generation

Due to the great potential in facilitating software development, code ge...

0 Hui Jiang, et al. ∙

research

∙ 05/13/2021

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

Text recognition is a popular topic for its broad applications. In this ...

0 Hui Jiang, et al. ∙

research

∙ 03/05/2021

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

In aspect-based sentiment analysis (ABSA), many neural models are equipp...

0 Jinsong Su, et al. ∙

research

∙ 06/21/2020

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

Community Question Answering (CQA) has become a primary means for people...

0 Zizhen Wang, et al. ∙

research

∙ 02/10/2020

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

In this paper, we have extended the well-established universal approxima...

11 Behnam Asadi, et al. ∙

research

∙ 07/30/2019

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

This paper presents a simple and computationally efficient approach for ...

0 Feng Wei, et al. ∙

research

∙ 05/30/2019

Bandlimiting Neural Networks Against Adversarial Attacks

In this paper, we study the adversarial attack and defence problem in de...

4 Yuping Lin, et al. ∙

research

∙ 05/24/2019

Content based News Recommendation via Shortest Entity Distance over Knowledge Graphs

Content-based news recommendation systems need to recommend news article...

5 Kevin Joseph, et al. ∙

research

∙ 04/05/2019

Effective Context and Fragment Feature Usage for Named Entity Recognition

In this paper, we explore a new approach to named entity recognition (NE...

0 Nargiza Nosirova, et al. ∙

research

∙ 04/05/2019

A Multi-task Learning Approach for Named Entity Recognition using Local Detection

Named entity recognition (NER) systems that perform well require task-re...

0 Nargiza Nosirova, et al. ∙

research

∙ 03/29/2019

A General FOFE-net Framework for Simple and Effective Question Answering over Knowledge Bases

Question answering over knowledge base (KB-QA) has recently become a pop...

4 Dekun Wu, et al. ∙

research

∙ 03/06/2019

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

In this paper, we present some theoretical work to explain why simple gr...

20 Hui Jiang, et al. ∙

research

∙ 02/23/2019

Fixed-Size Ordinally Forgetting Encoding Based Word Sense Disambiguation

In this paper, we present our method of using fixed-size ordinally forge...

4 Xi Zhu, et al. ∙

research

∙ 01/07/2019

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

In this work, we introduce the concept of bandlimiting into the theory o...

6 Hui Jiang, et al. ∙

research

∙ 11/16/2018

DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks

The past few years have witnessed the fast development of different regu...

24 Hengyue Pan, et al. ∙

research

∙ 09/10/2018

Exploring Machine Reading Comprehension with Explicit Knowledge

To apply general knowledge to machine reading comprehension (MRC), we pr...

6 Chao Wang, et al. ∙

research

∙ 09/08/2018

Simplified Hierarchical Recurrent Encoder-Decoder for Building End-To-End Dialogue Systems

As a generative model for building end-to-end dialogue systems, Hierarch...

0 Chao Wang, et al. ∙

research

∙ 07/21/2018

Distributed Layered Grant-Free Non-Orthogonal Multiple Access for Massive MTC

Grant-free transmission is considered as a promising technology to suppo...

0 Hui Jiang, et al. ∙

research

∙ 06/20/2018

Using Neural Network for Identifying Clickbaits in Online News Media

Online news media sometimes use misleading headlines to lure users to op...

0 Amin Omidvar, et al. ∙

research

∙ 03/09/2018

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

Small p-values are often required to be accurately estimated in large sc...

0 Yang Shi, et al. ∙

research

∙ 08/04/2017

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

The RepEval 2017 Shared Task aims to evaluate natural language understan...

0 Qian Chen, et al. ∙

research

∙ 03/14/2017

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

The last several years have seen intensive interest in exploring neural-...

0 Junbei Zhang, et al. ∙

research

∙ 11/13/2016

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

In this paper, we propose commonsense knowledge enhanced embeddings (KEE...

0 Quan Liu, et al. ∙

research

∙ 11/11/2016

Neural Networks Models for Entity Discovery and Linking

This paper describes the USTC_NELSLIP systems submitted to the Trilingua...

0 Dan Liu, et al. ∙

research

∙ 11/02/2016

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

In this paper, we study a novel approach for named entity recognition (N...

0 Mingbin Xu, et al. ∙

research

∙ 10/26/2016

Distraction-Based Neural Networks for Document Summarization

Distributed representation learned with neural networks has recently sho...

0 Qian Chen, et al. ∙

research

∙ 06/20/2016

Learning Convolutional Neural Networks using Hybrid Orthogonal Projection and Estimation

Convolutional neural networks (CNNs) have yielded the excellent performa...

0 Hengyue Pan, et al. ∙

research

∙ 04/30/2016

Higher Order Recurrent Neural Networks

In this paper, we study novel neural network structures to better model ...

0 Rohollah Soltani, et al. ∙

research

∙ 03/24/2016

Probabilistic Reasoning via Deep Learning: Neural Association Models

In this paper, we propose a new deep learning approach, called neural as...

1 Quan Liu, et al. ∙

research

∙ 03/24/2016

Part-of-Speech Relevance Weights for Learning Word Embeddings

This paper proposes a model to learn word embeddings with weighted conte...

0 Quan Liu, et al. ∙

research

∙ 02/16/2016

Generating images with recurrent adversarial networks

Gatys et al. (2015) showed that optimizing pixels to match features in a...

0 Daniel Jiwoong Im, et al. ∙

research

∙ 02/01/2016

A Deep Learning Based Fast Image Saliency Detection Algorithm

In this paper, we propose a fast deep learning method for object salienc...

0 Hengyue Pan, et al. ∙

research

∙ 12/28/2015

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

In this paper, we propose a novel neural network structure, namely feedf...

0 Shiliang Zhang, et al. ∙

research

∙ 10/09/2015

Feedforward Sequential Memory Neural Networks without Recurrent Feedback

We introduce a new structure for memory neural networks, called feedforw...

0 Shiliang Zhang, et al. ∙

research

∙ 06/24/2015

Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks

Financial news contains useful information on public companies and the m...

0 Yangtuo Peng, et al. ∙

research

∙ 05/06/2015

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

In this paper, we propose the new fixed-size ordinally-forgetting encodi...

0 Shiliang Zhang, et al. ∙

research

∙ 05/05/2015

Deep Learning for Object Saliency Detection and Image Segmentation

In this paper, we propose several novel deep learning methods for object...

0 Hengyue Pan, et al. ∙

research

∙ 02/03/2015

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

In this paper, we propose a novel model for high-dimensional data, calle...

0 Shiliang Zhang, et al. ∙

Hui Jiang

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