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Mini-batch cutting plane method for regularized risk minimization

Frontiers of Information technology & Electronic Engineering 2019年第11期20卷 1551-1563页

作者： Meng-long LU Lin-bo QIAO Da-wei FENG Dong-sheng LI Xi-cheng LU Science and Technology on Parallel and Distributed Laboratory National University of Defense TechnologyChangsha 410073China

Although concern has been recently expressed with regard to the solution to the non-convex problem, convex optimization is still important in machine learning, especially when the situation requires an interpretable model. Solution to the convex problem is a global minimum, and the final model can be explained mathematically. Typically, the convex problem is re-casted as a regularized risk minimization problem to prevent overfitting. The cutting plane method (CPM) is one of the best solvers for the convex problem, irrespective of whether the objective function is differentiable or not. However, CPM and its variants fail to adequately address large-scale data-intensive cases because these algorithms access the entire dataset in each iteration, which substantially increases the computational burden and memory cost. To alleviate this problem, we propose a novel algorithm named the mini-batch cutting plane method (MBCPM), which iterates with estimated cutting planes calculated on a small batch of sampled data and is capable of handling large-scale problems. Furthermore, the proposed MBCPM adopts a "sink" operation that detects and adjusts noisy estimations to guarantee convergence. Numerical experiments on extensive real-world datasets demonstrate the effectiveness of MBCPM, which is superior to the bundle methods for regularized risk minimization as well as popular stochastic gradient descent methods in terms of convergence speed.

关键词： Machine learning Optimization methods Gradient methods Cutting plane method

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parallelization of the training for face detection with transactional memory

Parallelization of the training for face detection with tran...

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2011 International Conference on Automation and Robotics, ICAR 2011

作者： Zeng, Kun Key Laboratory and Technology for National Defence of Parallel and Distributed Processing School of Computer National Univiersity of Defense Technology ChangSha Hunan China

ISBN: (纸本)9783642256455

The development of multi-core processor makes the parallelization of traditional sequential algorithms increasingly important. Meanwhile, transactional memory serves a good parallel programming model. This paper takes the advantage of software transactional memory to parallelize the Multi-Exit Asymmetric Adaboost algorithm for face detection. The parallel version is evaluated on three different implementations of software transactional memory. The experiment results show that the transactional memory based parallelization outperforms the traditional lock based approach. A speedup of nearly seven is achieved on a eight-core machine on an eight-core system. © 2011 Springer-Verlag.

关键词： Face recognition

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A survey on approaches and applications of knowledge representation learning 5

A survey on approaches and applications of knowledge represe...

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5th IEEE International Conference on Data science in Cyberspace, DSC 2020

作者： Li, Chenchen Li, Aiping Wang, Ye Tu, Hongkui Song, Yichen National University of Defense Technology College of Computer ChangSha China National University of Defense Technology College of Computer Science and Technology on Parallel and Distributed Processing Laboratory ChangSha China

ISBN: (纸本)9781728195582

Knowledge representation learning (KRL) is one of the important research topics in artificial intelligence and Natural language processing. It can efficiently calculate the semantics of entities and relations in a low-dimensional space, and effectively solve the problem of data sparsity, which significantly improve the performance of knowledge acquisition, fusion and reasoning and so on. Starting from the five perspectives of distance-based, semantic matching, bilinear-based, neural network model and additional information model, this paper first introduces the overall framework and specific model design, and then correspondingly introduces the experimental evaluation tasks, metrics and benchmark datasets of each model. On this basis, how to apply KRL to various downstream tasks is summarized. © 2020 IEEE.

关键词： Knowledge representation

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Skyline query processing on interval uncertain data

Skyline query processing on interval uncertain data

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作者： Li, Xiaoyong Wang, Yijie Li, Xiaoling Wang, Guangdong National Lab. for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha China

ISBN: (纸本)9780769546698

Many recent applications involve processing and analyzing uncertain data. Recently, several research efforts have addressed answering skyline queries efficiently on massive uncertain datasets. However, the research lacks methods to compute these queries on uncertain data, where each dimension of the uncertain object is represented as an interval or an exact value. In this paper, we extensively study the problem of skyline query on these interval based uncertain objects, which has never been studied before. We first model the problem of querying the skylines on interval datasets. Typically, we address two efficient algorithms with I/O optimal for the conventional interval skyline queries and constrained interval skyline queries, respectively. Extensive experiments demonstrate the efficiency of all our proposed algorithms. © 2012 IEEE.

关键词： Query processing

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A distributed paging RAM grid system for wide-area memory sharing 20

A distributed paging RAM grid system for wide-area memory sh...

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20th IEEE International parallel and distributed processing Symposium, IPDPS 2006

作者： Chu, Rui Xiao, Nong Zhuang, Yongzhen Liu, Yunhao Lu, Xicheng National Key Laboratory for Parallel and Distributed Processing ChangSha HuNan China Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

ISBN: (纸本)1424400546

Memory-intensive applications often suffer from the poor performance of disk swapping when memory is inadequate. Remote memory sharing schemes, which provide a remote memory that is faster than the local hard disk, are able to improve the performance of such applications. Due to the limitation of being applicable within single clusters only, however, most of the previous remote memory mechanisms, such as the network memory scheme, fail to be extendable into a large scale, distributed, heterogeneous, and dynamic environment. In this work, we propose a service-oriented grid memory sharing scheme, distributed Paging RAM Grid (DPRG). We study the properties and criteria of large scale memory sharing, and then design major operations and optimizations to fit the usage of grid systems. We collect trace from our grid environment, and evaluate DPRG through comprehensive trace-driven simulations. Results show that DPRG significantly outperforms existing remote memory sharing schemes and supports grid computing applications effectively. © 2006 IEEE.

关键词： Storage allocation (computer)

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Contrastive Learning with Diverse Samples 26

Contrastive Learning with Diverse Samples

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Wu, Lilei Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781643684369

Unsupervised visual representation learning has gained much attention from the computer vision community because of the recent contrastive learning achievements. Current work mainly adopts instance discrimination as the pretext task, which treats every single instance as a different class (negative) and uses a collection of data augmentation techniques to generate more examples (positive) for each class. The idea is straightforward and efficient but will generally cause similar instances to be classified into different classes. Such problem has been defined as 'class collision' in some previous works and is shown to hurt the representation ability. Motivated by this observation, we present a solution to address this issue by filtering similar negative examples from each mini-batch. Concretely, we model the problem as a Determinantal Point Process (DPP) so that similar instances can be filtered stochastically, and diverse samples are expected to be sampled for contrastive training. Besides, we further introduce a priority term for each instance, which indicates the hardness of its positives, so that instances with more hard positives are more likely to be sampled for contributing to the optimization. Our sampling can be efficiently implemented in a feed-forward manner and further accelerated by our encouraged complement DPP. Extensive experimental results demonstrate our priority over the standard setup of contrastive learning. © 2023 The Authors.

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A distributed Approach to Consistent Order Delivery of Concurrent Events in Asynchronous DVEs

A Distributed Approach to Consistent Order Delivery of Concu...

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2010 2nd International Conference on Education technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

作者： Hangjun Zhou Wei Zhang Yuxing Peng Sikun Li Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing Key Laboratory of Science and Technology for National Defence of Parallel and Distributed Processing

ISBN: (纸本)9781424463671;9781424463688;9781424463701

In large-scale asynchronous distributed virtual environments(DVEs), one of the difficult problems is to deliver the concurrent events in a consistent order at each node. Generally, the previous consistency control approaches can be classified into two categories: causal order and time stamped order. However, causal order approaches can merely preserve the cause-effect relation of events and time stamped order approaches seem intrinsically complex to be used in serverless large-scale asynchronous DVEs. In this paper, we proposed a novel distributed algorithm to identify the concurrent events and preserve the consistent order delivery of them at different nodes. Simulation studies are also carried out to compare the performance of this algorithm with that of the previous ones. The results show that the new algorithm can effectively deliver the concurrent events in consistent order at each node and is more efficient than the previous algorithms in large-scale asynchronous DVEs.

关键词： distributed virtual environments concurrent events distributed consistency algorithm

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Word-level BERT-CNN-RNN Model for Chinese Punctuation Restoration 6

Word-level BERT-CNN-RNN Model for Chinese Punctuation Restor...

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6th IEEE International Conference on Computer and Communications, ICCC 2020

作者： Zhang, Zhe Liu, Jie Chi, Lihua Chen, Xinhai Science and Technology on Parallel and Distributed Processing Laboratory Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728186351

Punctuation restoration in speech recognition has a wide range of application scenarios. Despite the widespread success of neural networks methods at performing punctuation restoration for English, there have been only limited attempts for Chinese punctuation restoration. Due to the differences between Chinese and English in terms of grammar and basic semantic units, existing methods for English is not suitable for Chinese punctuation restoration. To tackle this problem, we propose a hybrid model combining the kernel of Bidirectional Encoder Representations from Transformers (BERT), Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). This model employs a flexible structure and special CNN design which can extract word-level features for Chinese language. We compared the performance of the hybrid model with five widely-used punctuation restoration models on the public dataset. Experimental results demonstrate that our hybrid model is simple and efficient. It outperforms other models and achieves an accuracy of 69.1%. © 2020 IEEE.

关键词： Recurrent neural networks

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Modeling complex relationship paths for knowledge graph completion

Modeling complex relationship paths for knowledge graph comp...

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作者： Zeng, Ping Tan, Qingping Meng, Xiankai Zhang, Haoyu Xu, Jianjun College of Computer National University of Defense Technology Changsha China National Key Laboratory for Parallel and Distributed Processing Changsha China

Determining the validity of knowledge triples and filling in the missing entities or relationships in the knowledge graph are the crucial tasks for large-scale knowledge graph completion. So far, the main solutions use machine learning methods to learn the low-dimensional distributed representations of entities and relationships to complete the knowledge graph. Among them, translationmodels obtain excellent performance. However, the proposed translation models do not adequately consider the indirect relationships among entities, affecting the precision of the representation. Based on the long short-term memory neural network and existing translation models, we propose a multiple-module hybrid neural network model called TransP. By modeling the entity paths and their relationship paths, TransP can effectively excavate the indirect relationships among the entities, and thus, improve the quality of knowledge graph completion tasks. Experimental results show that TransP outperforms state-of-the-art models in the entity prediction task, and achieves the comparable performance with previous models in the relationship prediction task. © 2018 The Institute of Electronics, Information and Communication Engineers.

关键词： Knowledge graph

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RETRACTED ARTICLE: parallel block multigrid preconditioner for 3D navier-stokes equations on unstructured grids

RETRACTED ARTICLE: Parallel block multigrid preconditioner f...

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作者： Li, Zongzhe Lu, Yao Cao, Wei Wang, Yongxian Wang, Zhenghua Parallel and Distributed Processing for National Laboratory School of Computer Science National University of Defence Lechnology Changsha Hunan 410073 China

ISBN: (纸本)9781424472369

In this paper, we explore a parallel block multigrid preconditioner based on factorization of the coefficient matrix generated in three-dimensional unstructured grids system. This preconditioner is robust with respect to the magnitude of the mesh size and the coefficient complexity, apply smoothed aggregation algebraic multigrid which using block lower-upper symmetric Gauss-Seidel(BLU-SGS) as smoother to accelerate convergent rate. We focus on this method to three-dimensional, unstructured, large-scale, parallel and NS equations' steady-state simulations. The numerical results which conducted on NACA0012 airfoil indicate that nearly optimal convergence rates and computational complexity for parallel solutions, this method also provide high speedup and efficiency for a series of processors. © 2010 IEEE.

关键词： Navier Stokes equations

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