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Proceedings of the 23rd USENIX Conference on File and Storage Technologies

作者： Weijian Chen Shuibing He Haoyang Qu Xuechen Zhang The State Key Laboratory of Blockchain and Data Security Zhejiang University and Zhejiang Lab and Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security and Zhejiang Key Laboratory of Big Data Intelligent Computing Washington State University Vancouver

ISBN: (纸本)9781939133458

Distributed training of graph neural networks (GNNs) has become a crucial technique for processing large graphs. Prevalent GNN frameworks are model-centric, necessitating the transfer of massive graph vertex features to GNN models, which leads to a significant communication bottleneck. Recognizing that the model size is often significantly smaller than the feature size, we propose LeapGNN, a feature-centric framework that reverses this paradigm by bringing GNN models to vertex features. To make it truly effective, we first propose a micrograph-based training strategy that leverages a refined structure to enhance locality, combined with the model migration technique, to minimize remote feature retrieval. Then, we devise a feature pre-gathering approach that merges multiple fetch operations into a single one to eliminate redundant feature transmissions. Finally, we employ a micrograph-based merging method that adjusts the number of micrographs for each worker to minimize kernel switches and synchronization overhead. Our experimental results demonstrate that LeapGNN achieves a performance speedup of up to 4.2× compared to the state-of-the-art method, namely P3.

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Extended State Observer based Sliding Mode Control for a Class of Dynamic Systems with Input Constraint

Extended State Observer Based Sliding Mode Control for a Cla...

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2022 International Conference on Computer Engineering and Artificial Intelligence, ICCEAI 2022

作者： Yang, Jiaqianhao Wang, Min-Yi Xiong, Jing-Jing China Jiliang University College of Mechanical and Electrical Engineering China Jiliang University Key Lab. of Intelligent Mfg. Quality Big Data Tracing and Analysis of Zhejiang Province Hangzhou310018 China

ISBN: (数字)9781665468039

ISBN: (纸本)9781665468039

This paper is devoted to developing an extended state observer based sliding mode control method to realize the robust tracking control for a class of dynamic systems with input constraint. Firstly, in order to avoid system input saturation, auxiliary subsystem is introduced. Secondly, the extended state observer is used to estimate the total disturbance item of dynamic system including nonlinearity and external disturbance term. Then, by combining the Lyapunov stability theory, an extended state observer based sliding mode tracking controller is proposed to ensure that the tracking error system can be driven onto the sliding mode surface, and the asymptotic convergence of the tracking error is guaranteed. Finally, corresponding simulation results are carried out to illustrate the effectiveness of the proposed control method. © 2022 IEEE.

关键词： Sliding mode control

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Extended State Observer based Nonsingular Terminal Sliding Mode Control for a Class of Dynamic Systems

Extended State Observer Based Nonsingular Terminal Sliding M...

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2022 International Conference on Computer Engineering and Artificial Intelligence, ICCEAI 2022

作者： Wang, Min-Yi Yang, Jiaqianhao Xiong, Jing-Jing Key Lab. of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jil-iang University College of Mechanical and Electrical Engineering China Jiliang University Hangzhou310018 China

ISBN: (数字)9781665468039

ISBN: (纸本)9781665468039

For the problem of tracking control of a class of dynamic systems, an extended state observer based nonsingular terminal sliding mode control is proposed by sufficiently considering nonlinear characteristics and external disturbance. Firstly, in order to estimate and compensate the unknown total disturbance item of dynamic systems, the extended state observer is designed. Secondly, the nonsingular terminal sliding mode controller is designed to ensure that the tracking error converges to zero in finite time. Finally, abundant simulation results validate the effectiveness of the proposed control method. © 2022 IEEE.

关键词： Sliding mode control

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GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerators

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerato...

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IEEE Symposium on High-Performance Computer Architecture

作者： Siling Yang Shuibing He Wenjiong Wang Yanlong Yin Tong Wu Weijian Chen Xuechen Zhang Xian-He Sun Dan Feng The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Lab Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Zhejiang Key Laboratory of Big Data Intelligent Computing Washington State University Vancouver Illinois Institute of Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capability. However, existing accelerators can be severely underutilized even with pipelines, due to the oversight of the skewed execution times of various GCN stages and the ignorance of skewed degrees of graph vertices. In this work, we propose GOPIM, a GCN-oriented pipeline optimization for PIM accelerators to expedite GCN training. First, GOPIM proposes an ML-based scheme that allocates crossbar resources to the most needed stages to streamline the overall pipeline. Second, GOPIM utilizes a selective vertex updating technique that evenly distributes vertices on crossbars by interleaved mapping. These techniques collectively reduce the overall execution time without losing much accuracy. We also provide a practical architecture design for GOPIM. Our experimental results show that, GoPIM achieves up to 191 × speedup and 16.1 × energy saving, compared to the state-of-the-art work.

关键词： Training Accuracy Graph convolutional networks Pipelines Energy conservation Computer architecture Machine learning Parallel processing Optimization

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TrustUQA: A Trustful Framework for Unified Structured data Question Answering

arXiv

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arXiv 2024年

作者： Zhang, Wen Jin, Long Zhu, Yushan Chen, Jiaoyan Huang, Zhiwei Wang, Junjie Hua, Yin Liang, Lei Chen, Huajun Zhejiang University China University of Manchester United Kingdom Ant Group China ZJU-Ant Group Joint Lab of Knowledge Graph China Zhejiang Key Laboratory of Big Data Intelligent Computing China

Natural language question answering (QA) over structured data sources such as tables and knowledge graphs have been widely investigated, especially with Large Language Models (LLMs) in recent years. The main solutions include question to formal query parsing and retrieval-based answer generation. However, current methods of the former often suffer from weak generalization, failing to dealing with multi-types of sources, while the later is limited in trustfulness. In this paper, we propose TrustUQA, a trustful QA framework that can simultaneously support multiple types of structured data in a unified way. To this end, it adopts an LLM-friendly and unified knowledge representation method called Condition Graph (CG), and uses an LLM and demonstration-based two-level method for CG querying. For enhancement, it is also equipped with dynamic demonstration retrieval. We have evaluated TrustUQA with 5 benchmarks covering 3 types of structured data. It outperforms 2 existing unified structured data QA methods. In comparison with the baselines that are specific to one data type, it achieves state-of-the-art on 2 of the datasets. Further more, we have demonstrated the potential of our method for more general QA tasks, QA over mixed structured data and QA across structured data. The code is available at https://***/zjukg/TrustUQA. Copyright © 2024, The Authors. All rights reserved.

关键词： Structured Query Language

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Textile pattern recommendations with convolutional neural networks and autoencoder

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第18期35卷

作者： Mao, Kuang Wu, Sai He, Jiajia Huang, Haichao Yin, Yanlong Ren, Zujie Zhejiang Lab Res Ctr Next Generat Intelligent Comp Syst Hangzhou Peoples R China Key Lab Intelligent Comp Based Big Data Zhejiang Hangzhou Peoples R China Muri Technol Pte Ltd Hangzhou Peoples R China State Grid Zhejiang Elect Power Co Informat & Telecommun Branch Hangzhou Peoples R China

Textile pattern design is a time-consuming and tedious work. Mihui, our ongoing developing system, employs deep-learning techniques to automatically generate huge volumes of patterns with the help of human guidance. However, trained as a black box, Mihui cannot provide customized service for each individual designer who shows unique aesthetics preferences. In this article, we introduce the recommendation module of Mihui. The module forwards all generated pattern images to a deep encoding network, where images are mapped into 128-dimension vectors. For each user of Mihui, we create a profile by his/her purchased or downloaded history. A novel encoder network is proposed to learn a personal taste vector for each user, based on which, we recommend new patterns to him/her. Our records in Mihui show that the recommendation module effectively improve users' experience on Mihui.

关键词： autoencoder CNN recommendation algorithm textile pattern

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SPA: A Graph Spectral Alignment Perspective for Domain Adaptation

arXiv

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arXiv 2023年

作者： Xiao, Zhiqing Wang, Haobo Jin, Ying Feng, Lei Chen, Gang Huang, Fei Zhao, Junbo College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University China CUHK-SenseTime Joint Lab The Chinese University of Hong Kong Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore Alibaba Group China

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces;(ii)-we further develop a fine-grained message propagation module — upon a novel neighbor-aware self-training mechanism — in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://***/CrownX/SPA. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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SoLar: sinkhorn label refinery for imbalanced partial-label learning 22

SoLar: sinkhorn label refinery for imbalanced partial-label ...

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

作者： Haobo Wang Mingxuan Xia Yixuan Li Yuren Mao Lei Feng Gang Chen Junbo Zhao Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University Department of Computer Sciences University of Wisconsin-Madison College of Computer Science Chongqing University and Center for Advanced Intelligence Project RIKEN

ISBN: (纸本)9781713871088

Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label disambiguation methods have been proposed in this domain, they normally assume a class-balanced scenario that may not hold in many real-world applications. Empirically, we observe degenerated performance of the prior methods when facing the combinatorial challenge from the long-tailed distribution and partial-labeling. In this work, we first identify the major reasons that the prior work failed. We subsequently propose SoLar, a novel Optimal Transport-based framework that allows to refine the disambiguated labels towards matching the marginal class prior distribution. SoLar additionally incorporates a new and systematic mechanism for estimating the long-tailed class prior distribution under the PLL setup. Through extensive experiments, SoLar exhibits substantially superior results on standardized benchmarks compared to the previous state-of-the-art PLL methods.

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Collaboration based multi-label propagation for fraud detection 29

Collaboration based multi-label propagation for fraud detect...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Wang, Haobo Li, Zhao Huang, Jiaming Hui, Pengrui Liu, Weiwei Hu, Tianlei Chen, Gang Key Lab of Intelligent Computing Based Big Data of Zhejiang Province Zhejiang University China Alibaba Group Hangzhou China School of Computer Science Wuhan University China

ISBN: (纸本)9780999241165

Detecting fraud users, who fraudulently promote certain target items, is a challenging issue faced by e-commerce platforms. Generally, many fraud users have different spam behaviors simultaneously, e.g. spam transactions, clicks, reviews and so on. Existing solutions have two main limitations: 1) the correlations among multiple spam behaviors are neglected;2) large-scale computations are intractable when dealing with an enormous user set. To remedy these problems, this work proposes a collaboration based multi-label propagation (CMLP) algorithm. We first introduce a generic version that involves collaboration technique to exploit label correlations. Specifically, it breaks the final prediction into two parts: 1) its own prediction part;2) the prediction of others, i.e. collaborative part. Then, to accelerate it on large-scale e-commerce data, we propose a heterogeneous graph based variant that detects communities on the user-item graph directly. Both theoretical analysis and empirical results clearly validate the effectiveness and scalability of our proposals. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Forecasting

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SoLar: Sinkhorn label Refinery for Imbalanced Partial-label Learning 36

SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label ...

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36th Conference on Neural Information Processing Systems (NeurIPS)

作者： Wang, Haobo Xia, Mingxuan Li, Yixuan Mao, Yuren Feng, Lei Chen, Gang Zhao, Junbo Zhejiang Univ Key Lab Intelligent Comp based Big Data Zhejiang Hangzhou Peoples R China Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Univ Wisconsin Madison Dept Comp Sci Madison WI USA Chongqing Univ Coll Comp Sci Chongqing Peoples R China RIKEN Ctr Adv Intelligence Project Wako Japan

ISBN: (纸本)9781713871088

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