Jie Chen

Optimal design of controllers based on libraries and differential evolution

2015

This paper presents a new method for optimal design of controllers based on a "library" thought and differential evolution. At first an extensible component library and a performance criteria library are established in the approach. …

Explainable Neural Networks based on Additive Index Models

2018 · arXiv (Cornell University)

Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of the models makes them hard for the data analyst to …

FastGCN: Fast Learning with Graph Convolutional Networks via Importance\n Sampling

2018 · arXiv (Cornell University)

The graph convolutional networks (GCN) recently proposed by Kipf and Welling\nare an effective graph model for semi-supervised learning. This model, however,\nwas originally designed to be learned with the presence of both training and\ntest data. Moreover, …

Associated Task Scheduling Based on Dynamic Finish Time Prediction for Cloud Computing

2019

Cloud computing has emerged as an increasingly indispensable and highly demanded platform for various applications, as cloud computing allows for on demand resource provisioning and allocation. The associated tasks composing a job processed by cloud …

A multi-label classification method using a hierarchical and transparent representation for paper-reviewer recommendation

2019 · arXiv (Cornell University)

Paper-reviewer recommendation task is of significant academic importance for conference chairs and journal editors. How to effectively and accurately recommend reviewers for the submitted papers is a meaningful and still tough task. In this paper, …

Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning

2020 · IEEE Signal Processing Magazine

The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]. MTL is …

MTLH: Video QoE Monitoring for Encrypted Traffic via Multi-Task Learning with Hierarchy

2020

With the continuous growth of video traffic on the whole Internet, it is of vital importance for content providers to provide high-quality service. Monitoring the QoE (Quality of Experience) of video can help them to …

GAN-Based Dual Active Learning for Nosocomial Infection Detection

2021 · IEEE Transactions on Network Science and Engineering

Although monitor devices and information systems are increasingly connected across hospitals, but scenarios such as hospital acquired infections (HAIs) detection where data are extremely scare and imbalanced has remained to be solved. In this paper, …

Reform of English Writing Teaching Method Under the Background of Big Data and Artificial Intelligence

2023 · International Journal of e-Collaboration

In the new era of informatization, big data, and artificial intelligence, the education field has also set off a wave of informatization development of English education. Teaching writing has always been the focus and challenge …

Research on patent classification based on hierarchical label semantics

2022 · 2022 3rd International Conference on Education, Knowledge and Information Management (ICEKIM)

Patent classification is an essential task in patent information management and knowledge mining. Most existing studies are based on the textual content of individual patent texts (e.g., titles and abstracts) for classification, but the patent …

GLASU: A Communication-Efficient Algorithm for Federated Learning with Vertically Distributed Graph Data

2023 · arXiv (Cornell University)

Vertical federated learning (VFL) is a distributed learning paradigm, where computing clients collectively train a model based on the partial features of the same set of samples they possess. Current research on VFL focuses on …

Memory-Contrastive Unsupervised Domain Adaptation for Building Extraction of High-Resolution Remote Sensing Imagery

2023 · IEEE Transactions on Geoscience and Remote Sensing

Deep learning-based semantic segmentation has been widely applied for building extraction. However, due to the domain gap, the extraction of building in high-resolution remote sensing imagery is difficult when the model trained on a source …

A White-box Implementation of SM4 with Self-equivalence Encoding

2023 · The Computer Journal

Abstract White-box implementation can ensure the security of cryptographic algorithm in white-box attack environment without changing the inputs and outputs of the original algorithm. Most existing white-box implementations construct a series of lookup tables to …

Graph Neural Preconditioners for Iterative Solutions of Sparse Linear Systems

2024 · arXiv (Cornell University)

Preconditioning is at the heart of iterative solutions of large, sparse linear systems of equations in scientific disciplines. Several algebraic approaches, which access no information beyond the matrix itself, are widely studied and used, but …

Seed-CTS: Unleashing the Power of Tree Search for Superior Performance in Competitive Coding Tasks

2024 · arXiv (Cornell University)

Competition-level code generation tasks pose significant challenges for current state-of-the-art large language models (LLMs). For example, on the LiveCodeBench-Hard dataset, models such as O1-Mini and O1-Preview achieve pass@1 rates of only 0.366 and 0.143, respectively. …