b'Hongzhi Wang'

research

∙ 07/30/2023

IWEK: An Interpretable What-If Estimator for Database Knobs

The knobs of modern database management systems have significant impact ...

0 Yu Yan, et al. ∙

research

∙ 07/25/2023

Duet: efficient and scalable hybriD neUral rElation undersTanding

Learned cardinality estimation methods have achieved high precision comp...

0 Kaixin Zhang, et al. ∙

research

∙ 07/13/2023

MaxCorrMGNN: A Multi-Graph Neural Network Framework for Generalized Multimodal Fusion of Medical Data for Outcome Prediction

With the emergence of multimodal electronic health records, the evidence...

0 Niharika S. D'Souza, et al. ∙

research

∙ 05/30/2023

Contrastive Shapelet Learning for Unsupervised Multivariate Time Series Representation Learning

Recent studies have shown great promise in unsupervised representation l...

0 Zhiyu Liang, et al. ∙

research

∙ 05/28/2023

One stone, two birds: A lightweight multidimensional learned index with cardinality support

Innovative learning based structures have recently been proposed to tack...

0 Yingze Li, et al. ∙

research

∙ 04/11/2023

TodyNet: Temporal Dynamic Graph Neural Network for Multivariate Time Series Classification

Multivariate time series classification (MTSC) is an important data mini...

0 Huaiyuan Liu, et al. ∙

research

∙ 03/24/2023

UniTS: A Universal Time Series Analysis Framework with Self-supervised Representation Learning

Machine learning has emerged as a powerful tool for time series analysis...

0 Zhiyu Liang, et al. ∙

research

∙ 02/25/2023

DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning

Neural predictors currently show great potential in the performance eval...

0 Shenghe Zheng, et al. ∙

research

∙ 02/25/2023

TS-Cabinet: Hierarchical Storage for Cloud-Edge-End Time-series Database

Hierarchical data storage is crucial for cloud-edge-end time-series data...

0 Shuangshuang Cui, et al. ∙

research

∙ 02/21/2023

FedST: Federated Shapelet Transformation for Interpretable Time Series Classification

This paper studies how to develop accurate and interpretable time series...

0 Zhiyu Liang, et al. ∙

research

∙ 10/25/2022

Fusing Modalities by Multiplexed Graph Neural Networks for Outcome Prediction in Tuberculosis

In a complex disease such as tuberculosis, the evidence for the disease ...

0 Niharika S. D'Souza, et al. ∙

research

∙ 10/19/2022

Differentiable Self-Adaptive Learning Rate

Learning rate adaptation is a popular topic in machine learning. Gradien...

0 Bozhou Chen, et al. ∙

research

∙ 06/29/2022

AAE: An Active Auto-Estimator for Improving Graph Storage

Nowadays, graph becomes an increasingly popular model in many real appli...

0 Yu Yan, et al. ∙

research

∙ 06/18/2022

EEML: Ensemble Embedded Meta-learning

To accelerate learning process with few samples, meta-learning resorts t...

0 Geng Li, et al. ∙

research

∙ 05/20/2022

FIND:Explainable Framework for Meta-learning

Meta-learning is used to efficiently enable the automatic selection of m...

0 Xinyue Shao, et al. ∙

research

∙ 03/26/2022

AutoTS: Automatic Time Series Forecasting Model Design Based on Two-Stage Pruning

Automatic Time Series Forecasting (TSF) model design which aims to help ...

0 Chunnan Wang, et al. ∙

research

∙ 02/21/2022

Dynamic Relation Repairing for Knowledge Enhancement

Dynamic relation repair aims to efficiently validate and repair the inst...

0 Rui Kang, et al. ∙

research

∙ 01/24/2022

AutoMC: Automated Model Compression based on Domain Knowledge and Progressive search strategy

Model compression methods can reduce model complexity on the premise of ...

0 Chunnan Wang, et al. ∙

research

∙ 01/09/2022

TPAD: Identifying Effective Trajectory Predictions Under the Guidance of Trajectory Anomaly Detection Model

Trajectory Prediction (TP) is an important research topic in computer vi...

0 Chunnan Wang, et al. ∙

research

∙ 12/10/2021

Addressing Deep Learning Model Uncertainty in Long-Range Climate Forecasting with Late Fusion

Global warming leads to the increase in frequency and intensity of clima...

0 Ken C. L. Wong, et al. ∙

research

∙ 11/21/2021

Experience-Enhanced Learning: One Size Still does not Fit All in Automatic Database

Recent years, the database committee has attempted to develop automatic ...

0 Yu Yan, et al. ∙

research

∙ 09/21/2021

Search For Deep Graph Neural Networks

Current GNN-oriented NAS methods focus on the search for different layer...

0 Guosheng Feng, et al. ∙

research

∙ 04/09/2021

FL-AGCNS: Federated Learning Framework for Automatic Graph Convolutional Network Search

Recently, some Neural Architecture Search (NAS) techniques are proposed ...

0 Chunnan Wang, et al. ∙

research

∙ 01/08/2021

Approximate Query Processing for Group-By Queries based on Conditional Generative Models

The Group-By query is an important kind of query, which is common and wi...

0 Meifan Zhang, et al. ∙

research

∙ 12/13/2020

A Dual-Store Structure for Knowledge Graphs

To effectively manage increasing knowledge graphs in various domains, a ...

0 Zhixin Qi, et al. ∙

research

∙ 11/17/2020

Multi-SQL: An extensible multi-model data query language

Big data management aims to establish data hubs that support data in mul...

0 Yu Yan, et al. ∙

research

∙ 10/22/2020

Efficient RDF Graph Storage based on Reinforcement Learning

Knowledge graph is an important cornerstone of artificial intelligence. ...

0 Lei Zheng, et al. ∙

research

∙ 10/19/2020

Musings about Constructions of Efficient Latin Hypercube Designs with Flexible Run-sizes

Efficient Latin hypercube designs (LHDs), including maximin distance LHD...

0 Hongzhi Wang, et al. ∙

research

∙ 10/15/2020

Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Results

In recent years, many spatial-temporal graph convolutional network (STGC...

0 Chunnan Wang, et al. ∙

research

∙ 09/18/2020

EM-RBR: a reinforced framework for knowledge graph completion from reasoning perspective

Knowledge graph completion aims to predict the new links in given entiti...

0 Zhaochong An, et al. ∙

research

∙ 08/15/2020

Automatic Storage Structure Selection for hybrid Workload

In the use of database systems, the design of the storage engine and dat...

0 Hongzhi Wang, et al. ∙

research

∙ 07/23/2020

Reachability Queries with Label and Substructure Constraints on Knowledge Graphs

Since knowledge graphs (KGs) describe and model the relationships betwee...

0 Xiaolong Wan, et al. ∙

research

∙ 07/07/2020

Auto-CASH: Autonomous Classification Algorithm Selection with Deep Q-Network

The great amount of datasets generated by various data sources have pose...

0 Tianyu Mu, et al. ∙

research

∙ 07/06/2020

Multi-Objective Neural Architecture Search Based on Diverse Structures and Adaptive Recommendation

The search space of neural architecture search (NAS) for convolutional n...

0 Chunnan Wang, et al. ∙

research

∙ 06/16/2020

Index Selection for NoSQL Database with Deep Reinforcement Learning

We propose a new approach of NoSQL database index selection. For differe...

0 Shun Yao, et al. ∙

research

∙ 06/05/2020

Efficient Semi-External Depth-First Search

Computing Depth-First Search (DFS) results, i.e. depth-first order or DF...

0 Xiaolong Wan, et al. ∙

research

∙ 04/20/2020

Neural Network Segmentation of Cell Ultrastructure Using Incomplete Annotation

The Pancreatic beta cell is an important target in diabetes research. Fo...

0 John Paul Francis, et al. ∙

research

∙ 04/09/2020

ConsciousControlFlow(CCF): A Demonstration for conscious Artificial Intelligence

In this demo, we present ConsciousControlFlow(CCF), a prototype system t...

0 Hongzhi Wang, et al. ∙

research

∙ 03/05/2020

LAQP: Learning-based Approximate Query Processing

Querying on big data is a challenging task due to the rapid growth of da...

0 Meifan Zhang, et al. ∙

research

∙ 03/03/2020

Automatic Hyper-Parameter Optimization Based on Mapping Discovery from Data to Hyper-Parameters

Machine learning algorithms have made remarkable achievements in the fie...

0 Bozhou Chen, et al. ∙

research

∙ 12/02/2019

ExperienceThinking: Hyperparameter Optimization with Budget Constraints

The problem of hyperparameter optimization exists widely in the real lif...

0 Chunnan Wang, et al. ∙

research

∙ 10/24/2019

Auto-Model: Utilizing Research Papers and HPO Techniques to Deal with the CASH problem

In many fields, a mass of algorithms with completely different hyperpara...

0 Chunnan Wang, et al. ∙

research

∙ 09/21/2019

Automatic Weighted Matching Rectifying Rule Discovery for Data Repairing

Data repairing is a key problem in data cleaning which aims to uncover a...

0 Hiba Abu Ahmad, et al. ∙

research

∙ 08/22/2019

A General Data Renewal Model for Prediction Algorithms in Industrial Data Analytics

In industrial data analytics, one of the fundamental problems is to util...

0 Hongzhi Wang, et al. ∙

research

∙ 08/10/2019

Autoregressive-Model-Based Methods for Online Time Series Prediction with Missing Values: an Experimental Evaluation

Time series prediction with missing values is an important problem of ti...

0 Xi Chen, et al. ∙

research

∙ 08/09/2019

LSTM-based Flow Prediction

In this paper, a method of prediction on continuous time series variable...

0 Hongzhi Wang, et al. ∙

research

∙ 07/02/2019

Automated Detection and Type Classification of Central Venous Catheters in Chest X-Rays

Central venous catheters (CVCs) are commonly used in critical care setti...

0 Vaishnavi Subramanian, et al. ∙

research

∙ 04/26/2019

Regular Expression Matching on billion-nodes Graphs

In many applications, it is necessary to retrieve pairs of vertices with...

0 Hongzhi Wang, et al. ∙

research

∙ 04/25/2019

GPU-based Efficient Join Algorithms on Hadoop

The growing data has brought tremendous pressure for query processing an...

0 Hongzhi Wang, et al. ∙

research

∙ 08/05/2018

Error Detection in a Large-Scale Lexical Taxonomy

Knowledge base (KB) is an important aspect in artificial intelligence. O...

0 Sifan Liu, et al. ∙

Hongzhi Wang

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