Search Results for author: Jian Yu

Found 14 papers, 1 papers with code

Learning to Learn a Cold-start Sequential Recommender

no code implementations18 Oct 2021 Xiaowen Huang, Jitao Sang, Jian Yu, Changsheng Xu

The cold-start recommendation is an urgent problem in contemporary online applications.

Meta-Learning

Knowledge Graph-enhanced Sampling for Conversational Recommender System

no code implementations13 Oct 2021 Mengyuan Zhao, Xiaowen Huang, Lixi Zhu, Jitao Sang, Jian Yu

Then, two samplers are designed to enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining user preferences and reliable negative samples for updating recommender to achieve efficient acquisition of user preferences and model updating, and thus provide a powerful solution for CRS to deal with E&E problem.

Recommendation Systems

Homogeneous and Heterogeneous Relational Graph for Visible-infrared Person Re-identification

1 code implementation18 Sep 2021 Yujian Feng, Feng Chen, Jian Yu, Yimu Ji, Fei Wu, Shangdong Liu

In this paper, we separately model the homogenous structural relationship by a modality-specific graph within individual modality and then mine the heterogeneous structural correlation in these two modality-specific graphs.

Person Re-Identification

Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak

no code implementations11 Feb 2021 Muhammad Naeem, Jian Yu, Muhammad Aamir, Sajjad Ahmad Khan, Olayinka Adeleye, Zardad Khan

Then, the resulting significant variables concerning their lags are used in the regression model selected by the ARDL for predicting and forecasting the trend of the epidemic.

Decision Making Time Series

FDMT: A Benchmark Dataset for Fine-grained Domain Adaptation in Machine Translation

no code implementations31 Dec 2020 Wenhao Zhu, ShuJian Huang, Tong Pu, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen

To motivate a wide investigation in such settings, we present a real-world fine-grained domain adaptation task in machine translation (FDMT).

Autonomous Vehicles Domain Adaptation +2

Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation

no code implementations30 Mar 2020 Pei Zhang, Xu Zhang, Wei Chen, Jian Yu, Yan-Feng Wang, Deyi Xiong

In this paper, we propose a new framework to model cross-sentence dependencies by training neural machine translation (NMT) to predict both the target translation and surrounding sentences of a source sentence.

Document-level Document Level Machine Translation +3

A Generalization Theory based on Independent and Task-Identically Distributed Assumption

no code implementations28 Nov 2019 Guanhua Zheng, Jitao Sang, Houqiang Li, Jian Yu, Changsheng Xu

The derived generalization bound based on the ITID assumption identifies the significance of hypothesis invariance in guaranteeing generalization performance.

Image Classification

Attention, Please! Adversarial Defense via Attention Rectification and Preservation

no code implementations24 Nov 2018 Shangxi Wu, Jitao Sang, Kaiyuan Xu, Jiaming Zhang, Yanfeng Sun, Liping Jing, Jian Yu

This study provides a new understanding of the adversarial attack problem by examining the correlation between adversarial attack and visual attention change.

Adversarial Attack Adversarial Defense +1

Communication: Words and Conceptual Systems

no code implementations29 Jul 2015 Jian Yu

Word (phrase or symbol) representation is the fundamental problem for knowledge representation and understanding.

Generalized Categorization Axioms

no code implementations31 Mar 2015 Jian Yu

Categorization axioms have been proposed to axiomatizing clustering results, which offers a hint of bridging the difference between human recognition system and machine learning through an intuitive observation: an object should be assigned to its most similar category.

Density Estimation Dimensionality Reduction

Categorization Axioms for Clustering Results

no code implementations9 Mar 2014 Jian Yu, Zongben Xu

Cluster analysis has attracted more and more attention in the field of machine learning and data mining.

General Classification

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