检索结果-内蒙古大学图书馆

Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Yongzhe Jia Xuyun Zhang Bowen Liu Wanchun Dou Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen China Department of Computing Macquarie University Sydney NSW Australia Faculty of Data Science Shiga University Hikone Japan

Federated learning (FL) is an emerging machine learning paradigm that allows distributed participants to train a global model collaboratively. In edge networks, FL involves numerous edge devices updating the local models with the assistance of a central server while keeping the local data on the devices. However, edge devices are commonly resource-limited while neural network models used in FL require significant resource consumption to reach a satisfactory accuracy. Moreover, different devices are various in terms of system capabilities, and employing a uniform model on all devices leads to the derogation of model accuracy. Several pioneer work integrate model pruning techniques into the FL process and focus on reducing the resource consumption on edge devices, whereas neglecting the impact of model generality on accuracy. In our work, we propose FedACP, an efficient FL approach with adaptive channel pruning for edge devices to achieve better resource-accuracy trade-offs. Specifically, we design a two-phase channel pruning approach to adaptively prune local models, in which both the resource constraints and model generalities are taken into consideration. In addition, we also design an aggregation algorithm for aggregating heterogeneous local models produced by the two-phase channel pruning approach. To evaluate the performance of the FedACP, we implement it with a real FL framework FedML and compare it with several state-of-the-art methods. The experimental results show that FedACP achieves better model accuracy while simultaneously reducing 22.41% to 27.34% model parameters.

关键词：

来源：评论

学校读者我要写书评

暂无评论

swRodinia: A Benchmark Suite for Exploiting Architecture Properties of Sunway Processor 3rd

swRodinia: A Benchmark Suite for Exploiting Architecture Pr...

引用

3rd BenchCouncil International Symposium on Benchmarking, Measuring, and Optimizing, Bench 2020

作者： Chen, Bangduo Li, Mingzhen Yang, Hailong Luan, Zhongzhi Gan, Lin Yang, Guangwen Qian, Depei School of Computer Science and Engineering Beihang University Beijing100191 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi Jiangsu214125 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

ISBN: (纸本)9783030710576

The Sunway processor has been demonstrated with superior performance by various scientific applications, domain specific frameworks and numerical algorithms. However, the optimization techniques that can fully exploit the architecture features are usually buried deep in large code bases, which prevents average programmers to understand such optimization techniques. Thus, the existing complex software fails to provide guidance for more programs embracing the computation power of Sunway processor. In this paper, we build a benchmark suite swRodinia by porting and optimizing the well-known Rodinia benchmark on Sunway processor. Specifically, we demonstrate several optimization techniques by tailoring the benchmarks to better leverage the architecture features for higher performance. Moreover, based on the optimization experiences, we derive several useful insights from both software and hardware perspectives, that not only guide the better utilization of current Sunway processor, but also reveal the direction of hardware improvements for future Sunway processor. We open source the swRodinia benchmark suite and encourage the community to enhance the benchmark with us continuously. © 2021, Springer Nature Switzerland AG.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

Modeling Inter-Intra Heterogeneity for Graph Federated Learning 39

Modeling Inter-Intra Heterogeneity for Graph Federated Learn...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Engineering Research Center of Trustworthy AI Ministry of Education Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)157735897X

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

ESM all-atom: multi-scale protein language model for unified molecular modeling 24

ESM all-atom: multi-scale protein language model for unified...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Kangjie Zheng Siyu Long Tianyu Lu Junwei Yang Xinyu Dai Ming Zhang Zaiqing Nie Wei-Ying Ma Hao Zhou School of Computer Science National Key Laboratory for Multimedia Information Processing Peking University-Anker Embodied AI Lab Peking University Beijing China School of Artificial Intelligence National Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science Tsinghua University Institute for AI Industry Research (AIR) Tsinghua University and PharMolix Inc. Institute for AI Industry Research (AIR) Tsinghua University

Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of protein language models for applications involving both proteins and small molecules. In this paper, we propose ESMAA (ESM All-Atom), a novel approach that enables atom-scale and residue-scale unified molecular modeling. ESM-AA achieves this by pretraining on multi-scale code-switch protein sequences and utilizing a multi-scale position encoding to capture relationships among residues and atoms. Experimental results indicate that ESM-AA surpasses previous methods in protein-molecule tasks, demonstrating the full utilization of protein language models. Further investigations reveal that through unified molecular modeling, ESM-AA not only gains molecular knowledge but also retains its understanding of proteins.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fight Fire with Fire: How Much Can We Trust ChatGPT on Source Code-Related Tasks?

arXiv

引用

arXiv 2024年

作者： Yu, Xiao Liu, Lei Hu, Xing Keung, Jacky Wai Liu, Jin Xia, Xin State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Hangzhou China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Shanxi Xi’an China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science Wuhan University Wuhan China College of Computer Science and Technology Zhejiang University Hangzhou China

With the increasing utilization of large language models such as ChatGPT during software development, it has become crucial to verify the quality of code content it generates. Recent studies proposed utilizing ChatGPT as both a developer and tester for multi-agent collaborative software development. The multi-agent collaboration empowers ChatGPT to produce test reports for its generated code, enabling it to self-verify the code content and fix bugs based on these reports. However, these studies did not assess the effectiveness of the generated test reports in validating the code. Therefore, we conduct a comprehensive empirical investigation to evaluate ChatGPT’s self-verification capability in code generation, code completion, and program repair. We request ChatGPT to (1) generate correct code and then self-verify its correctness;(2) complete code without vulnerabilities and then self-verify for the presence of vulnerabilities;and (3) repair buggy code and then self-verify whether the bugs are resolved. Our findings on two code generation datasets, one code completion dataset, and two program repair datasets reveal the following observations: (1) ChatGPT often erroneously predicts its generated incorrect code as correct, its vulnerable completed code as non-vulnerable, and its failed program repairs as successful during its self-verification. (2) The self-contradictory hallucinations in ChatGPT’s behavior arise: (a) ChatGPT initially generates code that it believes to be correct but later predicts it to be incorrect;(b) ChatGPT initially generates code completions that it deems secure but later predicts them to be vulnerable;(c) ChatGPT initially outputs code that it considers successfully repaired but later predicts it to be buggy during its self-verification. (3) The self-verification capability of ChatGPT can be enhanced by asking the guiding question, which queries whether ChatGPT agrees with assertions about incorrectly generated or repaired code and vulnerabil

关键词： software design

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent Policy Transfer via Task Relationship Modeling

arXiv

引用

arXiv 2022年

作者： Qin, Rongjun Chen, Feng Wang, Tonghan Yuan, Lei Wu, Xiaoran Zhang, Zongzhang Zhang, Chongjie Yu, Yang National Key Laboratory of Novel Software Technology Nanjing China Polixir Technologies Nanjing China IIIS Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

Team adaptation to new cooperative tasks is a hallmark of human intelligence, which has yet to be fully realized in learning agents. Previous work on multi-agent transfer learning accommodate teams of different sizes, heavily relying on the generalization ability of neural networks for adapting to unseen tasks. We believe that the relationship among tasks provides the key information for policy adaptation. In this paper, we try to discover and exploit common structures among tasks for more efficient transfer, and propose to learn effect-based task representations as a common space of tasks, using an alternatively fixed training scheme. We demonstrate that the task representation can capture the relationship among tasks, and can generalize to unseen tasks. As a result, the proposed method can help transfer learned cooperation knowledge to new tasks after training on a few source tasks. We also find that fine-tuning the transferred policies help solve tasks that are hard to learn from scratch. Copyright © 2022, The Authors. All rights reserved.

关键词： Multi agent systems

来源：评论

学校读者我要写书评

暂无评论

LimTDD: A Compact Decision Diagram Integrating Tensor and Local Invertible Map Representations

arXiv

引用

arXiv 2025年

作者： Hong, Xin Dai, Aochu Gao, Dingchao Li, Sanjiang Ji, Zhengfeng Ying, Mingsheng and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Department of Computer Science and Technology Tsinghua University China Centre for Quantum Software and Information University of Technology Sydney Australia

Tensor Decision Diagrams (TDDs) provide an efficient structure for representing tensors by combining techniques from both tensor networks and decision diagrams, demonstrating competitive performance in quantum circuit simulation and verification. However, existing decision diagrams, including TDDs, fail to exploit isomorphisms within tensors, limiting their compression efficiency. This paper introduces Local Invertible Map Tensor Decision Diagrams (LimTDDs), an extension of TDD that integrates local invertible maps (LIMs) to achieve more compact representations. Unlike LIMDD, which applies Pauli operators to quantum states, LimTDD generalizes this approach using the XP-stabilizer group, enabling broader applicability. We develop efficient algorithms for normalization and key tensor operations, including slicing, addition, and contraction, essential for quantum circuit simulation and verification. Theoretical analysis shows that LimTDD surpasses TDD in compactness while maintaining its generality and offers exponential advantages over both TDD and LIMDD in the best-case scenarios. Experimental results validate these improvements, demonstrating LimTDD’s superior efficiency in quantum circuit simulation and functionality computation. Copyright © 2025, The Authors. All rights reserved.

关键词： Quantum electronics

来源：评论

学校读者我要写书评

暂无评论

Efficient algorithms for generalized linear bandits with heavy-tailed rewards 23

Efficient algorithms for generalized linear bandits with hea...

引用

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Bo Xue Yimu Wang Yuanyu Wan Jinfeng Yi Lijun Zhang Department of Computer Science City University of Hong Kong Hong Kong China and The City University of Hong Kong Shenzhen Research Institute Shenzhen China Cheriton School of Computer Science University of Waterloo Waterloo Canada School of Software Technology Zhejiang University Ningbo China JD AI Research Beijing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China and Peng Cheng Laboratory Shenzhen China

This paper investigates the problem of generalized linear bandits with heavy-tailed rewards, whose (1 + ε)-th moment is bounded for some ε ∈ (0,1]. Although there exist methods for generalized linear bandits, most of them focus on bounded or sub-Gaussian rewards and are not well-suited for many real-world scenarios, such as financial markets and web-advertising. To address this issue, we propose two novel algorithms based on truncation and mean of medians. These algorithms achieve an almost optimal regret bound of $\widetilde{O}(dT^{\frac{1}{1+\epsilon}})$, where d is the dimension of contextual information and T is the time horizon. Our truncation-based algorithm supports online learning, distinguishing it from existing truncation-based approaches. Additionally, our mean-of-medians-based algorithm requires only O(log T) rewards and one estimator per epoch, making it more practical. Moreover, our algorithms improve the regret bounds by a logarithmic factor compared to existing algorithms when ε =1. Numerical experimental results confirm the merits of our algorithms.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enabling Collaborative Clinical Diagnosis of Infectious Keratitis by Integrating Expert Knowledge and Interpretable Data-driven Intelligence

arXiv

引用

arXiv 2024年

作者： Fang, Zhengqing Zhou, Shuowen Yuan, Zhouhang Si, Yuxuan Li, Mengze Li, Jinxu Xu, Yesheng Xie, Wenjia Kuang, Kun Li, Yingming Wu, Fei Yao, Yu-Feng Department of Ophthalmology Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China School of Software Technology Zhejiang University Hangzhou China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Key Laboratory for Corneal Diseases Research of Zhejiang Province Hangzhou China

Although data-driven artificial intelligence (AI) in medical image diagnosis has shown impressive performance in silico, the lack of interpretability makes it difficult to incorporate the "black box" into clinicians' workflows. To make the diagnostic patterns learned from data understandable by clinicians, we develop an interpretable model, knowledge-guided diagnosis model (KGDM), that provides a visualized reasoning process containing AI-based biomarkers and retrieved cases that with the same diagnostic patterns. KGDM also embraced clinicians' prompts into the interpreted reasoning through human-AI interaction, leading to potentially enhanced safety and more accurate predictions. This study investigates the performance, interpretability, and clinical utility of KGDM in the diagnosis of infectious keratitis (IK), which is the leading cause of corneal blindness. The classification performance of KGDM is evaluated on a prospective validation dataset, an external testing dataset, and an publicly available testing dataset. The diagnostic odds ratios (DOR) of the interpreted AI-based biomarkers are effective, ranging from 3.011 to 35.233 and exhibit consistent diagnostic patterns with clinic experience. Moreover, a human-AI collaborative diagnosis test is conducted and the participants with collaboration achieved a performance exceeding that of both humans and AI. By synergistically integrating interpretability and interaction, this study facilitates the convergence of clinicians' expertise and data-driven intelligence. The promotion of inexperienced ophthalmologists with the aid of AI-based biomarkers, as well as increased AI prediction by intervention from experienced ones, demonstrate a promising diagnostic paradigm for infectious keratitis using KGDM, which holds the potential for extension to other diseases where experienced medical practitioners are limited and the safety of AI is concerned. © 2024, CC BY-NC-ND.

关键词： Statistical tests

来源：评论

学校读者我要写书评

暂无评论

UniGPS: A unified programming framework for distributed graph processing

arXiv

引用

arXiv 2021年

作者： Wang, Zhaokang Li, Junhong Qi, Yifan Zhu, Guanghui Yuan, Chunfeng Huang, Yihua State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

The industry and academia have proposed many distributed graph processing systems. However, the existing systems are not friendly enough for users like data analysts and algorithm engineers. On the one hand, the programing models and interfaces differ a lot in the existing systems, leading to high learning costs and program migration costs. On the other hand, these graph processing systems are tightly bound to the underlying distributed computing platforms, requiring users to be familiar with distributed computing. To improve the usability of distributed graph processing, we propose a unified distributed graph programming framework UniGPS. Firstly, we propose a unified cross-platform graph programming model VCProg for UniGPS. VCProg hides details of distributed computing from users. It is compatible with the popular graph programming models Pregel, GAS, and Push-Pull. VCProg programs can be executed by compatible distributed graph processing systems without modification, reducing the learning overheads of users. Secondly, UniGPS supports Python as the programming language. We propose an interprocess-communication-based execution environment isolation mechanism to enable Java/C++based systems to call user-defined methods written in Python. The experimental results show that UniGPS enables users to process big graphs beyond the memory capacity of a single machine without sacrificing usability. UniGPS shows near-linear data scalability and machine scalability. Copyright © 2021, The Authors. All rights reserved.

关键词： Python

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：