检索结果-内蒙古大学图书馆

arXiv 2023年

作者： Yin, Mingjia Wang, Hao Xu, Xiang Wu, Likang Zhao, Sirui Guo, Wei Liu, Yong Tang, Ruiming Lian, Defu Chen, Enhong Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Huawei Singapore Research Center Singapore Huawei Noah's Ark Lab Shenzhen China

The sequential recommendation system has been widely studied for its promising effectiveness in capturing dynamic preferences buried in users' sequential behaviors. Despite the considerable achievements, existing methods usually focus on intra-sequence modeling while overlooking exploiting global collaborative information by inter-sequence modeling, resulting in inferior recommendation performance. Therefore, previous works attempt to tackle this problem with a global collaborative item graph constructed by pre-defined rules. However, these methods neglect two crucial properties when capturing global collaborative information, i.e., adaptiveness and personalization, yielding sub-optimal user representations. To this end, we propose a graph-driven framework, named Adaptive and Personalized Graph Learning for Sequential Recommendation (APGL4SR), that incorporates adaptive and personalized global collaborative information into sequential recommendation systems. Specifically, we first learn an adaptive global graph among all items and capture global collaborative information with it in a self-supervised fashion, whose computational burden can be further alleviated by the proposed SVD-based accelerator. Furthermore, based on the graph, we propose to extract and utilize personalized item correlations in the form of relative positional encoding, which is a highly compatible manner of personalizing the utilization of global collaborative information. Finally, the entire framework is optimized in a multi-task learning paradigm, thus each part of APGL4SR can be mutually reinforced. As a generic framework, APGL4SR can not only outperform other baselines with significant margins, but also exhibit promising versatility, the ability to learn a meaningful global collaborative graph, and the ability to alleviate the dimensional collapse issue of item embeddings. The code is available at https://***/Graph-Team/APGL4SR. © 2023, CC BY.

关键词： Recommender systems

来源：评论

学校读者我要写书评

暂无评论

Multi-source Multi-label Causal Feature Selection

Multi-source Multi-label Causal Feature Selection

引用

IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Yun–an Wang Yaojin Lin Jie Wang Zhisen Wei Jinkun Chen Shaozi Li School of Computer Science Lab of Data Science and Intelligence Application Minnan Normal University Zhangzhou China Xiamen University Xiamen China

ISBN: (纸本)9781728194202

Multi-label causal feature selection plays a vital role in dealing with high-dimensional multi-label data, since the selected causal features signify the causal mechanism with respect to the class attribute. At present, the descriptive information of data is collected from different data sources in many practical applications. Therefore, there is a need of developing multi-source multi-label causal feature selection algorithms. In addition, existing multi-label causal feature selection algorithms cannot directly address multi-source heterogeneous distribution feature spaces. To address these problems, in this paper, we utilize the concept of causal invariance in causal inference. Firstly, we formulate the multi-label causal feature selection problem for multiple data sources as an invariant set search problem across the data sources. Secondly, we present the upper and lower bounds of the causal invariant set. Finally, we design a novel Multi-source Multi-label Causal Feature Selection (MMCFS) algorithm. Extensive experiments are conducted to verify the effectiveness of the proposed algorithm by comparing it with state-of-the-art methods on synthetic datasets.

关键词： Training Soft sensors Feature extraction Prediction algorithms Search problems Inference algorithms Information technology

来源：评论

学校读者我要写书评

暂无评论

Rejoinder on‘A review of distributed statistical inference’

引用

Statistical Theory and Related Fields 2022年第2期6卷 111-113页

作者： Yuan Gao Weidong Liu Hansheng Wang Xiaozhou Wang Yibo Yana Riquan Zhang School of Statistics and Key Laboratory of Advanced Theory and Application in Statistics and Data Science–MOE East China Normal UniversityShanghaiPeople’s Republic of China School of Mathematical Sciences–School of Life Sciences and Biotechnology–MOE Key Lab of Artifcial Intelligence Shanghai Jiao Tong UniversityShanghaiPeople’s Republic of China Guanghua School of Management Peking UniversityBeijingPeople’s Republic of China

We thank the editor,Professor Jun Shao,for orga-nizing this stimulating *** are grateful to all discussants for their insightful comments on our review article on the distributed statistical *** to the urgent need to process the datasets with massive sizes,various distributed computing methods have been proposed for the large-scale statistical ***,some important theoretical results were *** we want to give a relatively comprehensive overview on this hot topic,there are still some important works that have been missed in our review written over a year ***,we are glad to see the discussants provide reviews of some new works and *** hope that these discussions and our review would serve as a stimulus for further studies in this rapidly developing area.

关键词： editor discussions sizes

来源：评论

学校读者我要写书评

暂无评论

Hierarchical graph transformer with adaptive node sampling 22

Hierarchical graph transformer with adaptive node sampling

引用

Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Zaixi Zhang Qi Liu Qingyong Hu Chee-Kong Lee Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China and State Key Laboratory of Cognitive Intelligence Hefei Anhui China Hong Kong University of Science and Technology Tencent America

ISBN: (纸本)9781713871088

The Transformer architecture has achieved remarkable success in a number of domains including natural language processing and computer vision. However, when it comes to graph-structured data, transformers have not achieved competitive performance, especially on large graphs. In this paper, we identify the main deficiencies of current graph transformers: (1) Existing node sampling strategies in Graph Transformers are agnostic to the graph characteristics and the training process. (2) Most sampling strategies only focus on local neighbors and neglect the long-range dependencies in the graph. We conduct experimental investigations on synthetic datasets to show that existing sampling strategies are sub-optimal. To tackle the aforementioned problems, we formulate the optimization strategies of node sampling in Graph Transformer as an adversary bandit problem, where the rewards are related to the attention weights and can vary in the training procedure. Meanwhile, we propose a hierarchical attention scheme with graph coarsening to capture the long-range interactions while reducing computational complexity. Finally, we conduct extensive experiments on real-world datasets to demonstrate the superiority of our method over existing graph transformers and popular GNNs.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Surrogate-Assist Quasi-Affine Transformation Evolutionary for Multi-Objective Optimization of Empty Train Deployment on Heavy-Haul Railways

SSRN

引用

SSRN 2023年

作者： Du, Zhi-Gang Pan, Jeng-Shyang Xu, He-Ying Chu, Shu-Chuan Ni, Shao-Quan School of Transportation and Logistics Southwest Jiaotong University Sichuan Chengdu610031 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China National and Local Joint Engineering Lab for Integrated Transportation Intelligence Chengdu610031 China National Engineering Lab of Comprehensive Transportation Big Data Application Technology Chengdu610031 China

In recent years, surrogate-assisted evolutionary algorithms have become increasingly popular for solving expensive and time-consuming single-objective and multi-objective problems. However, in multi-objective optimization, the Pareto non-dominated solution set space can quickly become computationally intractable, making it challenging to select which samples to evaluate using the expensive fitness function. In this paper, we propose a novel surrogate-assist QUasi-Affine TRansformation Evolutionary algorithm (SA-QUATRE/MO) for solving multi-objective optimization problems. The SA-QUATRE/MO algorithm uses a radial basis neural network as a surrogate model to improve the speed of the algorithm operation by replacing the expensive fitness evaluation with the surrogate model. To ensure the excellence and diversity of the selected samples while keeping the archived sample space fixed, we propose a technique called Vector Space Sampling, which samples objective points in the current set of non-dominated solutions by dividing several sub-vector spaces. Additionally, we propose an uncertain sample infilling strategy to select samples for real fitness evaluation using a designed uncertainty function. We compare the SA-QUATRE/MO algorithm with three state-of-the-art algorithms for multi-objective problems in the DTLZ, MaF, and ZDT test function suites. Finally, we apply the SA-QUATRE/MO algorithm to solve the optimization problem of the empty train deployment at the loading end of a heavy-haul railway. We build a model based on the S12 section of a certain railway and optimize it using the SA-QUATRE/MO algorithm, demonstrating the effectiveness and practicality of our proposed approach. © 2023, The Authors. All rights reserved.

关键词： Vector spaces

来源：评论

学校读者我要写书评

暂无评论

A review of distributed statistical inference

arXiv

引用

arXiv 2023年

作者： Gao, Yuan Liu, Weidong Wang, Hansheng Wang, Xiaozhou Yan, Yibo Zhang, Riquan School of Statistics Key Laboratory of Advanced Theory and Application in Statistics and Data Science - MOE East China Normal University Shanghai China School of Mathematical Sciences Key Lab of Artificial Intelligence - MOE Shanghai Jiao Tong University Shanghai China Guanghua School of Management Peking University Beijing China

The rapid emergence of massive datasets in various fields poses a serious challenge to traditional statistical methods. Meanwhile, it provides opportunities for researchers to develop novel algorithms. Inspired by the idea of divide-and-conquer, various distributed frameworks for statistical estimation and inference have been proposed. They were developed to deal with large-scale statistical optimization problems. This paper aims to provide a comprehensive review for related literature. It includes parametric models, nonparametric models, and other frequently used models. Their key ideas and theoretical properties are summarized. The trade-off between communication cost and estimate precision together with other concerns are discussed. Copyright © 2023, The Authors. All rights reserved.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

An Equivariant Generative Framework for Molecular Graph-Structure Co-Design

arXiv

引用

arXiv 2023年

作者： Zhang, Zaixi Liu, Qi Lee, Chee-Kong Hsieh, Chang-Yu Chen, Enhong Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Anhui Hefei230026 China State Key Laboratory of Cognitive Intelligence Anhui Hefei230088 China Tencent America Palo AltoCA94306 United States Innovation Institute for Artificial Intelligence Medicine of Zhejiang University College of Pharmaceutical Sciences Zhejiang University Zhejiang Hangzhou310058 China

Designing molecules with desirable physiochemical properties and functionalities is a long-standing challenge in chemistry, material science, and drug discovery. Recently, machine learning-based generative models have emerged as promising approaches for de novo molecule design. However, further refinement of methodology is highly desired as most existing methods lack unified modeling of 2D topology and 3D geometry information and fail to effectively learn the structure-property relationship for molecule design. Here we present MolCode, a roto-translation equivariant generative framework for Molecular graph-structure Co-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Extensive experimental results show that MolCode outperforms previous methods on a series of challenging tasks including de novo molecule design, targeted molecule discovery, and structure-based drug design. Particularly, MolCode not only consistently generates valid (99.95% Validity) and diverse (98.75% Uniqueness) molecular graphs/structures with desirable properties, but also generate drug-like molecules with high affinity to target proteins (61.8% high affinity ratio), which demonstrates MolCode’s potential applications in material design and drug discovery. Our extensive investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design, and provides new insights into machine learning-based molecule representation and generation. © 2023, CC BY-NC-SA.

关键词： Molecules

来源：评论

学校读者我要写书评

暂无评论

Hierarchical Graph Transformer with Adaptive Node Sampling

arXiv

引用

arXiv 2022年

作者： Zhang, Zaixi Liu, Qi Hu, Qingyong Lee, Chee-Kong Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Hong Kong University of Science and Technology Hong Kong Tencent America United States

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification

arXiv

引用

arXiv 2022年

作者： Zhang, Kai Zhang, Kun Liu, Qi Huang, Zhenya Zhang, Mengdi Wu, Wei Cheng, Mingyue Chen, Enhong Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China State Key Laboratory of Cognitive Intelligence Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Meituan Beijing China Anhui Province Key Lab. of Big Data Analysis and Application University of S&T of China Hefei China

Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. Existing studies in this task attach more attention to the sequence modeling of sentences while largely ignoring the rich domain-invariant semantics embedded in graph structures (i.e., the part-of-speech tags and dependency relations). As an important aspect of exploring characteristics of language comprehension, adaptive graph representations have played an essential role in recent years. To this end, in the paper, we aim to explore the possibility of learning invariant semantic features from graph-like structures in CDSC. Specifically, we present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs. More specifically, we first raise a POS-Transformer module to extract sequential semantic features from the word sequences as well as the part-of-speech tags. Then, we design a Hybrid Graph Attention (HGAT) module to generate syntax-based semantic features by considering the transferable dependency relations. Finally, we devise an Integrated aDaptive Strategy (IDS) to guide the joint learning process of both modules. Extensive experiments on four public datasets indicate that GAST achieves comparable effectiveness to a range of state-of-the-art models. © 2022, CC BY.

关键词： Sentiment analysis

来源：评论

学校读者我要写书评

暂无评论

Full-Atom Protein Pocket Design via Iterative Refinement

arXiv

引用

arXiv 2023年

作者： Zhang, Zaixi Lu, Zepu Hao, Zhongkai Zitnik, Marinka Liu, Qi Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence Anhui Hefei China Dept. of Comp. Sci. and Tech. Institute for AI THBI Lab BNRist Center Tsinghua-Bosch Joint ML Center Tsinghua China Harvard University United States

The design of de novo functional proteins that bind specific ligand molecules is paramount in therapeutics and bio-engineering. A critical yet formidable task in this endeavor is the design of the protein pocket, which is the cavity region of the protein where the ligand binds. Current methods are plagued by inefficient generation, inadequate context modeling of the ligand molecule, and the inability to generate side-chain atoms. Here, we present the Full-Atom Iterative Refinement (FAIR) method, designed to address these challenges by facilitating the co-design of protein pocket sequences, specifically residue types, and their corresponding 3D structures. FAIR operates in two steps, proceeding in a coarse-to-fine manner (transitioning from protein backbone to atoms, including side chains) for a full-atom generation. In each iteration, all residue types and structures are simultaneously updated, a process termed full-shot refinement. In the initial stage, the residue types and backbone coordinates are refined using a hierarchical context encoder, complemented by two structure refinement modules that capture both inter-residue and pocket-ligand interactions. The subsequent stage delves deeper, modeling the side-chain atoms of the pockets and updating residue types to ensure sequence-structure congruence. Concurrently, the structure of the binding ligand is refined across iterations to accommodate its inherent flexibility. Comprehensive experiments show that FAIR surpasses existing methods in designing superior pocket sequences and structures, producing average improvement exceeding 10% in AAR and RMSD metrics. FAIR is available at https://***/zaixizhang/FAIR. © 2023, CC BY.

关键词： Atoms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：