Search Results for author: Jiang Bian

Found 76 papers, 26 papers with code

An Analysis of WordNet’s Coverage of Gender Identity Using Twitter and The National Transgender Discrimination Survey

no code implementations GWC 2016 Amanda Hicks, Michael Rutherford, Christiane Fellbaum, Jiang Bian

While gender identities in the Western world are typically regarded as binary, our previous work (Hicks et al., 2015) shows that there is more lexical variety of gender identity and the way people identify their gender.

Practical Strategies of Active Learning to Rank for Web Search

no code implementations20 May 2022 Qingzhong Wang, Haifang Li, Haoyi Xiong, Wen Wang, Jiang Bian, Yu Lu, Shuaiqiang Wang, Zhicong Cheng, Jingbo Zhou, Dawei Yin, Dejing Dou

To handle the diverse query requests from users at web-scale, Baidu has done tremendous efforts in understanding users' queries, retrieve relevant contents from a pool of trillions of webpages, and rank the most relevant webpages on the top of results.

Active Learning Learning-To-Rank

Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble

no code implementations19 May 2022 Zhengyu Yang, Kan Ren, Xufang Luo, Minghuan Liu, Weiqing Liu, Jiang Bian, Weinan Zhang, Dongsheng Li

Considering the great performance of ensemble methods on both accuracy and generalization in supervised learning (SL), we design a robust and applicable method named Ensemble Proximal Policy Optimization (EPPO), which learns ensemble policies in an end-to-end manner.

reinforcement-learning

DEPTS: Deep Expansion Learning for Periodic Time Series Forecasting

1 code implementation ICLR 2022 Wei Fan, Shun Zheng, Xiaohan Yi, Wei Cao, Yanjie Fu, Jiang Bian, Tie-Yan Liu

However, the complicated dependencies of the PTS signal on its inherent periodicity as well as the sophisticated composition of various periods hinder the performance of PTS forecasting.

Time Series Time Series Forecasting

Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting

no code implementations18 Feb 2022 Lin Huang, Lijun Wu, Jia Zhang, Jiang Bian, Tie-Yan Liu

How to discover the useful implicit relation between entities and effectively utilize the relations for each entity under various circumstances is crucial.

Graph Learning Time Series +1

AF$_2$: Adaptive Focus Framework for Aerial Imagery Segmentation

no code implementations18 Feb 2022 Lin Huang, Qiyuan Dong, Lijun Wu, Jia Zhang, Jiang Bian, Tie-Yan Liu

As a specific semantic segmentation task, aerial imagery segmentation has been widely employed in high spatial resolution (HSR) remote sensing images understanding.

Semantic Segmentation

Learning Physics-Informed Neural Networks without Stacked Back-propagation

no code implementations18 Feb 2022 Di He, Wenlei Shi, Shanda Li, Xiaotian Gao, Jia Zhang, Jiang Bian, LiWei Wang, Tie-Yan Liu

Physics-Informed Neural Network (PINN) has become a commonly used machine learning approach to solve partial differential equations (PDE).

GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records

no code implementations2 Feb 2022 Xi Yang, Nima PourNejatian, Hoo Chang Shin, Kaleb E Smith, Christopher Parisien, Colin Compas, Cheryl Martin, Mona G Flores, Ying Zhang, Tanja Magoc, Christopher A Harle, Gloria Lipori, Duane A Mitchell, William R Hogan, Elizabeth A Shenkman, Jiang Bian, Yonghui Wu

We developed GatorTron models from scratch using the BERT architecture of different sizes including 345 million, 3. 9 billion, and 8. 9 billion parameters, compared GatorTron with three existing transformer models in the clinical and biomedical domain on 5 different clinical NLP tasks including clinical concept extraction, relation extraction, semantic textual similarity, natural language inference, and medical question answering, to examine how large transformer models could help clinical NLP at different linguistic levels.

Clinical Concept Extraction Language Modelling +4

DDG-DA: Data Distribution Generation for Predictable Concept Drift Adaptation

1 code implementation11 Jan 2022 Wendi Li, Xiao Yang, Weiqing Liu, Yingce Xia, Jiang Bian

To handle concept drift, previous methods first detect when/where the concept drift happens and then adapt models to fit the distribution of the latest data.

Stock Prediction

SHGNN: Structure-Aware Heterogeneous Graph Neural Network

1 code implementation12 Dec 2021 Wentao Xu, Yingce Xia, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path.

Graph Embedding Node Classification

KGE-CL: Contrastive Learning of Knowledge Graph Embeddings

1 code implementation9 Dec 2021 Wentao Xu, Zhiping Luo, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

To address this problem, we propose a simple yet efficient contrastive learning framework for knowledge graph embeddings, which can shorten the semantic distance of the related entities and entity-relation couples in different triples and thus improve the expressiveness of knowledge graph embeddings.

Knowledge Graph Embedding Knowledge Graph Embeddings +4

Towards Inter-class and Intra-class Imbalance in Class-imbalanced Learning

1 code implementation24 Nov 2021 Zhining Liu, Pengfei Wei, Zhepei Wei, Boyang Yu, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang

We also present a detailed discussion and analysis about the pros and cons of different inter/intra-class balancing strategies based on DUBE .

Ensemble Learning

IMBENS: Ensemble Class-imbalanced Learning in Python

1 code implementation24 Nov 2021 Zhining Liu, Zhepei Wei, Erxin Yu, Qiang Huang, Kai Guo, Boyang Yu, Zhaonian Cai, Hangting Ye, Wei Cao, Jiang Bian, Pengfei Wei, Jing Jiang, Yi Chang

imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for quick implementing and deploying ensemble learning algorithms on class-imbalanced data.

Ensemble Learning

HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information

1 code implementation26 Oct 2021 Wentao Xu, Weiqing Liu, Lewen Wang, Yingce Xia, Jiang Bian, Jian Yin, Tie-Yan Liu

To overcome the shortcomings of previous work, we proposed a novel stock trend forecasting framework that can adequately mine the concept-oriented shared information from predefined concepts and hidden concepts.

Multi-Agent Reinforcement Learning with Shared Resource in Inventory Management

no code implementations29 Sep 2021 Mingxiao Feng, Guozi Liu, Li Zhao, Lei Song, Jiang Bian, Tao Qin, Wengang Zhou, Houqiang Li, Tie-Yan Liu

We consider inventory management (IM) problem for a single store with a large number of SKUs (stock keeping units) in this paper, where we need to make replenishment decisions for each SKU to balance its supply and demand.

Multi-agent Reinforcement Learning reinforcement-learning

Deep Ensemble Policy Learning

no code implementations29 Sep 2021 Zhengyu Yang, Kan Ren, Xufang Luo, Weiqing Liu, Jiang Bian, Weinan Zhang, Dongsheng Li

Ensemble learning, which can consistently improve the prediction performance in supervised learning, has drawn increasing attentions in reinforcement learning (RL).

Ensemble Learning

Instance-wise Graph-based Framework for Multivariate Time Series Forecasting

1 code implementation14 Sep 2021 Wentao Xu, Weiqing Liu, Jiang Bian, Jian Yin, Tie-Yan Liu

In this paper, we propose a simple yet efficient instance-wise graph-based framework to utilize the inter-dependencies of different variables at different time stamps for multivariate time series forecasting.

Multivariate Time Series Forecasting Time Series

Explaining Algorithmic Fairness Through Fairness-Aware Causal Path Decomposition

no code implementations11 Aug 2021 Weishen Pan, Sen Cui, Jiang Bian, ChangShui Zhang, Fei Wang

Algorithmic fairness has aroused considerable interests in data mining and machine learning communities recently.

Fairness Feature Importance

A Study of Social and Behavioral Determinants of Health in Lung Cancer Patients Using Transformers-based Natural Language Processing Models

no code implementations10 Aug 2021 Zehao Yu, Xi Yang, Chong Dang, Songzi Wu, Prakash Adekkanattu, Jyotishman Pathak, Thomas J. George, William R. Hogan, Yi Guo, Jiang Bian, Yonghui Wu

In this study, we examined two state-of-the-art transformer-based NLP models, including BERT and RoBERTa, to extract SBDoH concepts from clinical narratives, applied the best performing model to extract SBDoH concepts on a lung cancer screening patient cohort, and examined the difference of SBDoH information between NLP extracted results and structured EHRs (SBDoH information captured in standard vocabularies such as the International Classification of Diseases codes).

Clinical Relation Extraction Using Transformer-based Models

1 code implementation19 Jul 2021 Xi Yang, Zehao Yu, Yi Guo, Jiang Bian, Yonghui Wu

The goal of this study is to systematically explore three widely used transformer-based models (i. e., BERT, RoBERTa, and XLNet) for clinical relation extraction and develop an open-source package with clinical pre-trained transformer-based models to facilitate information extraction in the clinical domain.

Classification Multi-class Classification +1

Deep Risk Model: A Deep Learning Solution for Mining Latent Risk Factors to Improve Covariance Matrix Estimation

no code implementations12 Jul 2021 Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian

Modeling and managing portfolio risk is perhaps the most important step to achieve growing and preserving investment performance.

Assessing putative bias in prediction of anti-microbial resistance from real-world genotyping data under explicit causal assumptions

no code implementations6 Jul 2021 Mattia Prosperi, Simone Marini, Christina Boucher, Jiang Bian

Whole genome sequencing (WGS) is quickly becoming the customary means for identification of antimicrobial resistance (AMR) due to its ability to obtain high resolution information about the genes and mechanisms that are causing resistance and driving pathogen mobility.

Learning Multiple Stock Trading Patterns with Temporal Routing Adaptor and Optimal Transport

1 code implementation24 Jun 2021 Hengxu Lin, Dong Zhou, Weiqing Liu, Jiang Bian

In this paper, we propose a novel architecture, Temporal Routing Adaptor (TRA), to empower existing stock prediction models with the ability to model multiple stock trading patterns.

Stock Prediction

Deep Learning Models in Detection of Dietary Supplement Adverse Event Signals from Twitter

no code implementations21 Jun 2021 Yefeng Wang, Yunpeng Zhao, Jiang Bian, Rui Zhang

We chose the best performing models in each task to assemble an end-to-end deep learning pipeline to detect DS AE signals and compared the results to the known DS AEs from a DS knowledge base (i. e., iDISK).

Relation Extraction Word Embeddings

Deep Subdomain Adaptation Network for Image Classification

1 code implementation17 Jun 2021 Yongchun Zhu, Fuzhen Zhuang, Jindong Wang, Guolin Ke, Jingwu Chen, Jiang Bian, Hui Xiong, Qing He

The adaptation can be achieved easily with most feed-forward network models by extending them with LMMD loss, which can be trained efficiently via back-propagation.

Classification Domain Adaptation +4

Impact of pandemic fatigue on the spread of COVID-19: a mathematical modelling study

no code implementations9 Apr 2021 Disheng Tang, Wei Cao, Jiang Bian, Tie-Yan Liu, Zhifeng Gao, Shun Zheng, Jue Liu

We used a stochastic metapopulation model with a hierarchical structure and fitted the model to the positive cases in the US from the start of outbreak to the end of 2020.

A Conversational Agent System for Dietary Supplements Use

no code implementations4 Apr 2021 Esha Singh, Anu Bompelli, Ruyuan Wan, Jiang Bian, Serguei Pakhomov, Rui Zhang

Dietary supplements (DS) have been widely used by consumers, but the information around the efficacy and safety of DS is disparate or incomplete, thus creating barriers for consumers to find information effectively.

Interpretable Deep Learning: Interpretation, Interpretability, Trustworthiness, and Beyond

1 code implementation19 Mar 2021 Xuhong LI, Haoyi Xiong, Xingjian Li, Xuanyu Wu, Xiao Zhang, Ji Liu, Jiang Bian, Dejing Dou

Then, to understand the results of interpretation, we also survey the performance metrics for evaluating interpretation algorithms.

Adversarial Robustness

REST: Relational Event-driven Stock Trend Forecasting

no code implementations15 Feb 2021 Wentao Xu, Weiqing Liu, Chang Xu, Jiang Bian, Jian Yin, Tie-Yan Liu

To remedy the first shortcoming, we propose to model the stock context and learn the effect of event information on the stocks under different contexts.

Universal Trading for Order Execution with Oracle Policy Distillation

no code implementations28 Jan 2021 Yuchen Fang, Kan Ren, Weiqing Liu, Dong Zhou, Weinan Zhang, Jiang Bian, Yong Yu, Tie-Yan Liu

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument.

Algorithmic Trading reinforcement-learning

Applications of artificial intelligence in drug development using real-world data

no code implementations22 Jan 2021 Zhaoyi Chen, Xiong Liu, William Hogan, Elizabeth Shenkman, Jiang Bian

The US Food and Drug Administration (FDA) has been actively promoting the use of real-world data (RWD) in drug development.

Event Detection

Dynamic Graph Representation Learning with Fourier Temporal State Embedding

1 code implementation1 Jan 2021 Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian

In this work, we present a new method named Fourier Temporal State Embedding (FTSE) to address the temporal information in dynamic graph representation learning.

Graph Embedding Graph Representation Learning

LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks

no code implementations1 Jan 2021 Yihan He, Wei Cao, Shun Zheng, Zhifeng Gao, Jiang Bian

In recent years, research communities have been developing stochastic sampling methods to handle large graphs when it is unreal to put the whole graph into a single batch.

Graph Representation Learning

Cooperative Policy Learning with Pre-trained Heterogeneous Observation Representations

1 code implementation24 Dec 2020 Wenlei Shi, Xinran Wei, Jia Zhang, Xiaoyuan Ni, Arthur Jiang, Jiang Bian, Tie-Yan Liu

While adopting complex GNN models with more informative message passing and aggregation mechanisms can obviously benefit heterogeneous vertex representations and cooperative policy learning, it could, on the other hand, increase the training difficulty of MARL and demand more intense and direct reward signals compared to the original global reward.

Graph Attention Multi-agent Reinforcement Learning

ADD: Augmented Disentanglement Distillation Framework for Improving Stock Trend Forecasting

1 code implementation11 Dec 2020 Hongshun Tang, Lijun Wu, Weiqing Liu, Jiang Bian

Stock trend forecasting has become a popular research direction that attracts widespread attention in the financial field.

Disentanglement

COSEA: Convolutional Code Search with Layer-wise Attention

no code implementations19 Oct 2020 Hao Wang, Jia Zhang, Yingce Xia, Jiang Bian, Chao Zhang, Tie-Yan Liu

However, most existing studies overlook the code's intrinsic structural logic, which indeed contains a wealth of semantic information, and fails to capture intrinsic features of codes.

Code Search

MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler

2 code implementations NeurIPS 2020 Zhining Liu, Pengfei Wei, Jing Jiang, Wei Cao, Jiang Bian, Yi Chang

This makes MESA generally applicable to most of the existing learning models and the meta-sampler can be efficiently applied to new tasks.

imbalanced classification Meta-Learning

Qlib: An AI-oriented Quantitative Investment Platform

1 code implementation22 Sep 2020 Xiao Yang, Weiqing Liu, Dong Zhou, Jiang Bian, Tie-Yan Liu

Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.

Portfolio Optimization Stock Market Prediction

Learning to Reweight with Deep Interactions

no code implementations9 Jul 2020 Yang Fan, Yingce Xia, Lijun Wu, Shufang Xie, Weiqing Liu, Jiang Bian, Tao Qin, Xiang-Yang Li

Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc.

Image Classification Machine Translation +1

Measuring Model Complexity of Neural Networks with Curve Activation Functions

no code implementations16 Jun 2020 Xia Hu, Weiqing Liu, Jiang Bian, Jian Pei

Our results demonstrate that the occurrence of overfitting is positively correlated with the increase of model complexity during training.

MC-BERT: Efficient Language Pre-Training via a Meta Controller

1 code implementation10 Jun 2020 Zhenhui Xu, Linyuan Gong, Guolin Ke, Di He, Shuxin Zheng, Li-Wei Wang, Jiang Bian, Tie-Yan Liu

Pre-trained contextual representations (e. g., BERT) have become the foundation to achieve state-of-the-art results on many NLP tasks.

Cloze Test Language Modelling +3

Invertible Image Rescaling

3 code implementations ECCV 2020 Mingqing Xiao, Shuxin Zheng, Chang Liu, Yaolong Wang, Di He, Guolin Ke, Jiang Bian, Zhouchen Lin, Tie-Yan Liu

High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, meanwhile the post-upscaling is adpoted to recover the original resolutions or the details in the zoom-in images.

Image Super-Resolution

Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets

no code implementations26 Mar 2020 Yunpeng Zhao, Mattia Prosperi, Tianchen Lyu, Yi Guo, Jiang Bian

Results show that crowdsourcing is useful to create high-quality annotations and active learning helps in reducing the number of required tweets, although there was no substantial difference among the strategies tested.

Active Learning General Classification

Federated Learning for Healthcare Informatics

no code implementations13 Nov 2019 Jie Xu, Benjamin S. Glicksberg, Chang Su, Peter Walker, Jiang Bian, Fei Wang

With the rapid development of computer software and hardware technologies, more and more healthcare data are becoming readily available from clinical institutions, patients, insurance companies and pharmaceutical industries, among others.

Federated Learning

Fully Parameterized Quantile Function for Distributional Reinforcement Learning

4 code implementations NeurIPS 2019 Derek Yang, Li Zhao, Zichuan Lin, Tao Qin, Jiang Bian, Tie-Yan Liu

The key challenge in practical distributional RL algorithms lies in how to parameterize estimated distributions so as to better approximate the true continuous distribution.

Ranked #3 on Atari Games on Atari 2600 Skiing (using extra training data)

Atari Games Distributional Reinforcement Learning +1

Identifying Cancer Patients at Risk for Heart Failure Using Machine Learning Methods

no code implementations1 Oct 2019 Xi Yang, Yan Gong, Nida Waheed, Keith March, Jiang Bian, William R. Hogan, Yonghui Wu

Early detection of cancer patients at risk for cardiotoxicity before cardiotoxic treatments and providing preventive measures are potential solutions to improve cancer patients's quality of life.

Independence-aware Advantage Estimation

no code implementations25 Sep 2019 Pushi Zhang, Li Zhao, Guoqing Liu, Jiang Bian, Minglie Huang, Tao Qin, Tie-Yan Liu

Most of existing advantage function estimation methods in reinforcement learning suffer from the problem of high variance, which scales unfavorably with the time horizon.

reinforcement-learning

Demonstration Actor Critic

no code implementations25 Sep 2019 Guoqing Liu, Li Zhao, Pushi Zhang, Jiang Bian, Tao Qin, Nenghai Yu, Tie-Yan Liu

One approach leverages demonstration data in a supervised manner, which is simple and direct, but can only provide supervision signal over those states seen in the demonstrations.

Self-paced Ensemble for Highly Imbalanced Massive Data Classification

1 code implementation8 Sep 2019 Zhining Liu, Wei Cao, Zhifeng Gao, Jiang Bian, Hechang Chen, Yi Chang, Tie-Yan Liu

To tackle this problem, we conduct deep investigations into the nature of class imbalance, which reveals that not only the disproportion between classes, but also other difficulties embedded in the nature of data, especially, noises and class overlapping, prevent us from learning effective classifiers.

Classification General Classification +1

LightMC: A Dynamic and Efficient Multiclass Decomposition Algorithm

no code implementations25 Aug 2019 Ziyu Liu, Guolin Ke, Jiang Bian, Tie-Yan Liu

Instead of using fixed coding matrix and decoding strategy, LightMC uses a differentiable decoding strategy, which enables it to dynamically optimize the coding matrix and decoding strategy, toward increasing the overall accuracy of multiclass classification, via back propagation jointly with the training of base learners in an iterative way.

Classification General Classification

Light Multi-segment Activation for Model Compression

2 code implementations16 Jul 2019 Zhenhui Xu, Guolin Ke, Jia Zhang, Jiang Bian, Tie-Yan Liu

Inspired by the nature of the expressiveness ability in Neural Networks, we propose to use multi-segment activation, which can significantly improve the expressiveness ability with very little cost, in the compact student model.

Knowledge Distillation Model Compression +1

Mining Twitter to Assess the Determinants of Health Behavior towards Human Papillomavirus Vaccination in the United States

no code implementations6 Jul 2019 Hansi Zhang, Christopher Wheldon, Adam G. Dunn, Cui Tao, Jinhai Huo, Rui Zhang, Mattia Prosperi, Yi Guo, Jiang Bian

We applied topic modeling to discover major themes, and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS).

TabNN: A Universal Neural Network Solution for Tabular Data

no code implementations ICLR 2019 Guolin Ke, Jia Zhang, Zhenhui Xu, Jiang Bian, Tie-Yan Liu

Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all.

Enhancing Prediction Models for One-Year Mortality in Patients with Acute Myocardial Infarction and Post Myocardial Infarction Syndrome

no code implementations28 Apr 2019 Seyedeh Neelufar Payrovnaziri, Laura A. Barrett, Daniel Bis, Jiang Bian, Zhe He

Predicting the risk of mortality for patients with acute myocardial infarction (AMI) using electronic health records (EHRs) data can help identify risky patients who might need more tailored care.

A survey on trajectory clustering analysis

no code implementations20 Feb 2018 Jiang Bian, Dayong Tian, Yuanyan Tang, DaCheng Tao

This paper comprehensively surveys the development of trajectory clustering.

CSWA: Aggregation-Free Spatial-Temporal Community Sensing

no code implementations15 Nov 2017 Jiang Bian, Haoyi Xiong, Yanjie Fu, Sajal K. Das

In this paper, we present a novel community sensing paradigm -- {C}ommunity {S}ensing {W}ithout {A}ggregation}.

Compressive Sensing Distributed Optimization

Slim-DP: A Light Communication Data Parallelism for DNN

no code implementations27 Sep 2017 Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu

However, with the increasing size of DNN models and the large number of workers in practice, this typical data parallelism cannot achieve satisfactory training acceleration, since it usually suffers from the heavy communication cost due to transferring huge amount of information between workers and the parameter server.

Dual Supervised Learning

1 code implementation ICML 2017 Yingce Xia, Tao Qin, Wei Chen, Jiang Bian, Nenghai Yu, Tie-Yan Liu

Many supervised learning tasks are emerged in dual forms, e. g., English-to-French translation vs. French-to-English translation, speech recognition vs. text to speech, and image classification vs. image generation.

General Classification Image Classification +5

FWDA: a Fast Wishart Discriminant Analysis with its Application to Electronic Health Records Data Classification

no code implementations25 Apr 2017 Haoyi Xiong, Wei Cheng, Wenqing Hu, Jiang Bian, Zhishan Guo

Classical LDA for EHR data classification, however, suffers from two handicaps: the ill-posed estimation of LDA parameters (e. g., covariance matrix), and the "linear inseparability" of EHR data.

Classification General Classification

Learning What Data to Learn

no code implementations28 Feb 2017 Yang Fan, Fei Tian, Tao Qin, Jiang Bian, Tie-Yan Liu

Machine learning is essentially the sciences of playing with data.

Image Classification

Ensemble-Compression: A New Method for Parallel Training of Deep Neural Networks

no code implementations2 Jun 2016 Shizhao Sun, Wei Chen, Jiang Bian, Xiaoguang Liu, Tie-Yan Liu

In this framework, we propose to aggregate the local models by ensemble, i. e., averaging the outputs of local models instead of the parameters.

Model Compression

Solving Verbal Comprehension Questions in IQ Test by Knowledge-Powered Word Embedding

no code implementations29 May 2015 Huazheng Wang, Fei Tian, Bin Gao, Jiang Bian, Tie-Yan Liu

Second, we obtain distributed representations of words and relations by leveraging a novel word embedding method that considers the multi-sense nature of words and the relational knowledge among words (or their senses) contained in dictionaries.

KNET: A General Framework for Learning Word Embedding using Morphological Knowledge

no code implementations7 Jul 2014 Qing Cui, Bin Gao, Jiang Bian, Siyu Qiu, Tie-Yan Liu

In particular, we introduce a novel neural network architecture called KNET that leverages both contextual information and morphological word similarity built based on morphological knowledge to learn word embeddings.

Information Retrieval Word Embeddings +1

WordRep: A Benchmark for Research on Learning Word Representations

no code implementations7 Jul 2014 Bin Gao, Jiang Bian, Tie-Yan Liu

In this paper, we describe the details of the WordRep collection and show how to use it in different types of machine learning research related to word embedding.

Word Embeddings

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