检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

2,976 篇 会议
38 篇 期刊文献
7 册 图书

馆藏范围

3,021 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,758 篇 工学
- 1,406 篇 计算机科学与技术...
- 684 篇 软件工程
- 490 篇 电气工程
- 397 篇 信息与通信工程
- 203 篇 控制科学与工程
- 130 篇 生物工程
- 77 篇 电子科学与技术（可...
- 65 篇 生物医学工程（可授...
- 58 篇 机械工程
- 54 篇 仪器科学与技术
- 48 篇 光学工程
- 33 篇 网络空间安全
- 30 篇 动力工程及工程热...
- 23 篇 安全科学与工程
- 22 篇 材料科学与工程（可...
- 22 篇 化学工程与技术
- 20 篇 测绘科学与技术
- 19 篇 石油与天然气工程
515 篇 理学
- 224 篇 物理学
- 189 篇 数学
- 143 篇 生物学
- 58 篇 统计学（可授理学、...
- 40 篇 系统科学
- 27 篇 化学
202 篇 管理学
- 134 篇 管理科学与工程(可...
- 81 篇 图书情报与档案管...
- 31 篇 工商管理
135 篇 医学
- 112 篇 临床医学
- 38 篇 基础医学(可授医学...
37 篇 法学
- 20 篇 社会学
18 篇 文学
14 篇 农学
10 篇 教育学
9 篇 经济学
2 篇 军事学

主题

591 篇 neural networks
180 篇 training
169 篇 artificial neura...
168 篇 deep learning
142 篇 computational mo...
129 篇 feature extracti...
123 篇 convolutional ne...
115 篇 neural network
88 篇 machine learning
87 篇 neurons
86 篇 parallel process...
79 篇 deep neural netw...
75 篇 computer archite...
74 篇 distributed comp...
73 篇 accuracy
65 篇 signal processin...
62 篇 distributed proc...
61 篇 data models
60 篇 recurrent neural...
51 篇 convolutional ne...

机构

11 篇 institute of inf...
9 篇 school of cyber ...
8 篇 univ chinese aca...
8 篇 natl univ def te...
7 篇 college of compu...
7 篇 carnegie mellon ...
7 篇 department of co...
7 篇 national laborat...
7 篇 beijing universi...
7 篇 natl univ def te...
6 篇 shanghai jiao to...
6 篇 the islamic univ...
5 篇 science and tech...
5 篇 ibm tj watson re...
5 篇 school of inform...
5 篇 king saud univer...
5 篇 shandong normal ...
5 篇 kaust thuwal
5 篇 university of ch...
5 篇 natl univ def te...

作者

8 篇 li dongsheng
8 篇 dou yong
8 篇 jie liu
6 篇 richtarik peter
6 篇 ribeiro alejandr...
6 篇 liu jie
6 篇 niu xin
6 篇 yong dou
6 篇 shulian yang
6 篇 zhang wei
5 篇 ryabinin max
5 篇 segarra santiago
5 篇 lai zhiquan
5 篇 ding bo
5 篇 qiao peng
5 篇 xu jiren
5 篇 kokkinos yiannis
5 篇 peng yuxing
5 篇 wang huaimin
5 篇 li aiping

语言

2,814 篇 英文
199 篇 其他
11 篇 中文
2 篇 土耳其文

检索条件"任意字段=Conference on Neural Network and Distributed Processing"

共 3021 条记录，以下是1521-1530 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

A Stacking Multi-Learning Ensemble Model for Predicting Near Real Time Energy Consumption Demand of Residential Buildings 15

A Stacking Multi-Learning Ensemble Model for Predicting Near...

引用

IEEE 15th International conference on Intelligent Computer Communication and processing (ICCP)

作者： Vesa, Andreea Valeria Ghitescu, Nicoleta Pop, Claudia Antal, Marcel Cioara, Tudor Anghel, Ionut Salomie, Ioan Tech Univ Cluj Napoca Dept Comp Sci Distributed Syst Res Lab Cluj Napoca Romania

ISBN: (纸本)9781728149141

The aim of this paper is to present a novel energy consumption forecasting solution for predicting energy demand at the level of residential buildings based on their historical consumption profile for a seamless integration with the sessionbased Energy Markets developed within Smart Energy Grids that integrate renewable energy sources. To overcome the drawbacks and lack of accuracy of existing prediction models, a stacked multi-learning ensemble model is proposed combining Gradient Boosting Regression, Multi-Layer neural networks and Long Short Term Memory networks followed by a Linear Regressor for forecasting residential energy demands both at individual and aggregated levels. The proposed ensemble predictor is evaluated using the open-access UK-DALE dataset containing historical energy traces fill-5 households spreading over several years, obtaining a best MADE, of 1.59%, a RMSE of 6.19k141/i and a MAE of 5.60k1Vh on the aggregated dataset, proving the high accuracy and stability of the proposed solution as well as the feasibility of using ensemble models for residential building energy demand forecast for integration with session-based energy marketplaces.

关键词： energy forecasting smart grid machine learning deep learning Gradient Boosting Regression Multilayer Perceptron Long Short-Term Memory network

来源：评论

学校读者我要写书评

暂无评论

Implementation of Bayesian inference in distributed neural networks 26

Implementation of Bayesian inference in distributed neural n...

引用

26th Euromicro International conference on Parallel, distributed, and network-Based processing (PDP)

作者： Yu, Zhaofei Hang, Tiejun Liu, Jian K. Peking Univ Sch Elect Engn & Comp Sci Beijing Peoples R China Graz Univ Technol Inst Theoret Comp Sci Graz Austria

ISBN: (纸本)9781538649756

Numerous neuroscience experiments have suggested that the cognitive process of human brain is realized as probability reasoning and further modeled as Bayesian inference. It is still unclear how Bayesian inference could be implemented by neural underpinnings in the brain. Here we present a novel Bayesian inference algorithm based on importance sampling. By distributed sampling through a deep tree structure with simple and stackable basic motifs for any given neural circuit, one can perform local inference while guaranteeing the accuracy of global inference. We show that these task-independent motifs can be used in parallel for fast inference without iteration and scale-limitation. Furthermore, experimental simulations with a small-scale neural network demonstrate that our distributed sampling-based algorithm, consisting with our theoretical analysis, can approximate Bayesian inference. Taken all together, we provide a proof-of-principle to use distributed neural networks to implement Bayesian inference, which gives a road-map for large-scale Bayesian network implementation based on spiking neural networks with computer hardwares, including neuromorphic chips.

关键词： Bayesian inference distributed neural network importance sampling neural implementation

来源：评论

学校读者我要写书评

暂无评论

Towards Precise End-to-end Semi-Supervised Human Head Detection network

Towards Precise End-to-end Semi-Supervised Human Head Detect...

引用

International Joint conference on neural networks (IJCNN)

作者： Rongchun Li Junjie Zhang Yuntao Liu Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169279

Head detection, as a fundamental task in practice for many head-related problems, requires an enormous number of annotated boxes to maintain the performance. To alleviate the time and cost of labeling each image in the dataset, we propose an end-to-end semi-supervised head detection frame-work, which shows competitive results with only a small set of data. Specifically, under the setting of semi-supervised, we introduce a weak boxes generate branch and a weak boxes refine branch to produce pseudo ground truth label for unlabeled images with the guidance of annotated images. The weak boxes generate branch is embedded in the detection framework taking the proposals as input and outputting the initial weak boxes that coarsely locate the place of the head. Then, the weak boxes refine branch adjusts the weak boxes more accurate gradually by training a transferred sub-network with the established relation between proposals, weak boxes and labeled boxes. In the training process, we jointly train the two branches in an end-to-end manner, which can generate better pseudo bounding boxes with a small dataset online to avoid over-fitting and obtain a more precise head detector. The results on the public head detection benchmark Brainwash and SCUT-HEAD show the effectiveness of our method.

关键词： Head Proposals Training Detectors Object detection Feature extraction Task analysis

来源：评论

学校读者我要写书评

暂无评论

Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity 20

Accelerating Sparse DNN Models without Hardware-Support via ...

引用

Supercomputing conference

作者： Cong Guo Bo Yang Hsueh Jingwen Leng Yuxian Qiu Yue Guan Zehuan Wang Xiaoying Jia Xipeng Li Minyi Guo Yuhao Zhu Shanghai Jiao Tong University NVIDIA Shanghai Qi Zhi Institute Shanghai Jiao Tong University University of Rochester

ISBN: (数字)9781728199986

ISBN: (纸本)9781728199993

network pruning can reduce the high computation cost of deep neural network (DNN) models. However, to maintain their accuracies, sparse models often carry randomly-distributed weights, leading to irregular computations. Consequently, sparse models cannot achieve meaningful speedup on commodity hardware (e.g., GPU) built for dense matrix computations. As such, prior works usually modify or design completely new sparsity-optimized architectures for exploiting sparsity. We propose an algorithm-software co-designed pruning method that achieves latency speedups on existing dense architectures. Our work builds upon the insight that the matrix multiplication generally breaks the large matrix into multiple smaller tiles for parallel execution. We propose a tiling-friendly “tile-wise” sparsity pattern, which maintains a regular pattern at the tile level for efficient execution but allows for irregular, arbitrary pruning at the global scale to maintain the high accuracy. We implement and evaluate the sparsity pattern on GPU tensor core, achieving a 1.95× speedup over the dense model.

关键词： Tensors Computational modeling High performance computing neural networks Graphics processing units Computer architecture Sparse matrices

来源：评论

学校读者我要写书评

暂无评论

On the Acceleration of Deep Learning Model Parallelism With Staleness

On the Acceleration of Deep Learning Model Parallelism With ...

引用

conference on Computer Vision and Pattern Recognition (CVPR)

作者： An Xu Zhouyuan Huo Heng Huang Electrical and Computer Engineering Department University of Pittsburgh PA USA JD Finance America Corporation Mountain View CA USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Training the deep convolutional neural network for computer vision problems is slow and inefficient, especially when it is large and distributed across multiple devices. The inefficiency is caused by the backpropagation algorithm's forward locking, backward locking, and update locking problems. Existing solutions for acceleration either can only handle one locking problem or lead to severe accuracy loss or memory inefficiency. Moreover, none of them consider the straggler problem among devices. In this paper, we propose Layer-wise Staleness and a novel efficient training algorithm, Diversely Stale Parameters (DSP), to address these challenges. We also analyze the convergence of DSP with two popular gradient-based methods and prove that both of them are guaranteed to converge to critical points for non-convex problems. Finally, extensive experimental results on training deep learning models demonstrate that our proposed DSP algorithm can achieve significant training speedup with stronger robustness than compared methods.

关键词： Training Parallel processing Computational modeling neural networks Stochastic processes Acceleration Convergence

来源：评论

学校读者我要写书评

暂无评论

A Many-Core Accelerator Design for On-Chip Deep Reinforcement Learning

A Many-Core Accelerator Design for On-Chip Deep Reinforcemen...

引用

IEEE International conference on Computer-Aided Design

作者： Ying Wang Mengdi Wang Bing Li Huawei Li Xiaowei Li State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing P.R. China Capital Normal University Beijing P.R. China

ISBN: (数字)9781665423243

Deep Reinforcement Learning (DRL) is substantially resource-consuming, and it requires large-scale distributed computing-nodes to learn complicated tasks, like video-game and Go play. This work attempts to down-scale a distributed DRL system into a specialized many-core chip and achieve energy-efficient on-chip DRL. With the customized network-on-Chip that handles the communication of on-chip data and control-signals, we proposed a Synchronous Asynchronous RL Architecture (SARLA) and the according many-core chip that completely avoids the unnecessary data duplication and synchronization activities in multi-node RL systems. In evaluation, the SARLA system achieves considerable energy-efficiency boost over the GPU-based implementations for typical DRL workloads built with OpenAI-gym.

关键词： System-on-chip Computer architecture Reinforcement learning neural networks Servers Parallel processing Training

来源：评论

学校读者我要写书评

暂无评论

Reducing global reductions in large-scale distributed training 19

Reducing global reductions in large-scale distributed traini...

引用

48th International conference on Parallel processing (ICPP)

作者： Cong, Guojing Yang, Chih-Chieh Zhou, Fan IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Baidu USA Sunnyville CA USA

ISBN: (纸本)9781450371964

Current large-scale training of deep neural networks typically employs synchronous stochastic gradient descent that incurs large communication overhead. Instead of optimizing reduction routines as done in recent studies, we propose algorithms that do not require frequent global reductions. We first show that reducing the global reduction frequency works as an effective regularization technique that improves generalization of adaptive optimizers. We then propose an algorithm that reduces the global reduction frequency by employing local reductions on a subset of learners. In addition, to maximize the effect of reduction on convergence, we introduce reduction momentum that further accelerates convergence. Our experiment with the CIFAR-10 dataset shows that for the K-step averaging algorithm extremely sparse reductions help bridge the generalization gap. With 6 GPUs, in comparison to regular synchronous implementations, our implementation reduces more than 99% of global reductions. Further, we show that with 32 GPUs, our implementation reduces the number of global reductions by half. Experimenting with the ImageNet-1K dataset, we show that combining local reductions with global reductions and applying reduction momentum can further reduce global reductions by up to 62% for the same validation accuracy achieved in comparison to K-step averaging. With 400 GPUs global reduction frequency is reduced to once per 102K samples.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

distributed LARGE neural network WITH CENTRALIZED EQUIVALENCE

DISTRIBUTED LARGE NEURAL NETWORK WITH CENTRALIZED EQUIVALENC...

引用

IEEE International conference on Acoustics, Speech and Signal processing (ICASSP)

作者： Liang, Xinyue Javid, Alireza M. Skoglund, Mikael Chatterjee, Saikat KTH Royal Inst Technol Sch Elect Engn Dept Informat Sci & Engn Stockholm Sweden

ISBN: (纸本)9781538646588

In this article, we develop a distributed algorithm for learning a large neural network that is deep and wide. We consider a scenario where the training dataset is not available in a single processing node, but distributed among several nodes. We show that a recently proposed large neural network architecture called progressive learning network (PLN) can be trained in a distributed setup with centralized equivalence. That means we would get the same result if the data be available in a single node. Using a distributed convex optimization method called alternating-direction-method-of-multipliers (ADMM), we perform training of PLN in the distributed setup.

关键词： distributed learning neural networks data parallelism convex optimization

来源：评论

学校读者我要写书评

暂无评论

5th International conference on Information Management and Big Data, SIMBig 2018

5th International Conference on Information Management and B...

引用

5th International conference on Information Management and Big Data, SIMBig 2018

ISBN: (纸本)9783030116798

The proceedings contain 34 papers. The special focus in this conference is on Information Management and Big Data. The topics include: Aerial scene classification and information retrieval via fast kernel based fuzzy C-means clustering;a case study of library data management: A new method to analyze borrowing behavior;sparkmach: A distributed data processing system based on automated machine learning for big data;deep dive into authorship verification of email messages with convolutional neural network;monitoring of air quality with low-cost electrochemical sensors and the use of artificial neural networks for the atmospheric pollutants concentration levels prediction;data mining algorithms for risk detection in bank loans;DETECTOR: Automatic detection system for terrorist attack trajectories;car monitoring system in apartments’ Garages by small autonomous car using deep learning;a framework for analytical approaches to combine interpretable models;which is the tallest building in Europe? Representing and reasoning about knowledge;processing Quechua and Guarani historical texts query expansion at character and word level for information retrieval;Topic modeling applied to business research: A latent dirichlet allocation (LDA)-based classification for organization studies;using neural network for identifying clickbaits in online news media;spanish named entity recognition in the biomedical domain;ontology modeling of the Estonian traffic act for self-driving buses;thought off-line sanitization methods for bank transactions;big data for development: An approach as a state government capacity in the countries;towards real-time automatic stress detection for office workplaces;soTesTeR: Software testing techniques’ recommender system using a collaborative approach;crowdsourcing high-quality structured data.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient Estimation of Ontology Entities distributed Representations 14th

Efficient Estimation of Ontology Entities Distributed Repres...

引用

14th International conference on Knowledge Management in Organizations (KMO) - Synergistic Role of Knowledge Management in Organization

作者： Benarab, Achref Sun, Jianguo Refoufi, Allaoua Guan, Jian Harbin Engn Univ Coll Comp Sci & Technol Harbin Peoples R China Univ Setif 1 Comp Sci Dept Setif Algeria

ISBN: (纸本)9783030214517;9783030214500

Ontologies have been used as a form of knowledge representation in different fields such as artificial intelligence, semantic web and natural language processing. The success caused by deep learning in recent years as a major upheaval in the field of artificial intelligence depends greatly on the data representation, since these representations can encode different types of hidden syntactic and semantic relationships in data, making their use very common in data science tasks. Ontologies do not escape this trend, applying deep learning techniques in the ontology-engineering field has heightened the need to learn and generate representations of the ontological data, which will allow ontologies to be exploited by such models and algorithms and thus automatizing different ontology-engineering tasks. This paper presents a novel approach for learning low dimensional continuous feature representations for ontology entities based on the semantic embedded in ontologies, using a multi-input feed-forward neural network trained using noise contrastive estimation technique. Semantically similar ontology entities will have relatively close corresponding representations in the projection space. Thus, the relationships between the ontology entities representations mirrors exactly the semantic relations between the corresponding entities in the source ontology.

关键词： Ontology entities distributed representations Continuous vector representations Feature representation Concept embeddings neural networks

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共303页 << < 149 150 151 152 153 154 155 156 157 158 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：