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

2,959 篇 会议
39 篇 期刊文献
7 册 图书

馆藏范围

3,005 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,760 篇 工学
- 1,407 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
- 79 篇 电子科学与技术（可...
- 65 篇 生物医学工程（可授...
- 59 篇 机械工程
- 54 篇 仪器科学与技术
- 49 篇 光学工程
- 34 篇 网络空间安全
- 30 篇 动力工程及工程热...
- 24 篇 材料科学与工程（可...
- 24 篇 安全科学与工程
- 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
517 篇 理学
- 226 篇 物理学
- 190 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
203 篇 管理学
- 135 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

590 篇 neural networks
173 篇 training
168 篇 artificial neura...
165 篇 deep learning
135 篇 computational mo...
124 篇 feature extracti...
115 篇 neural network
114 篇 convolutional ne...
88 篇 machine learning
87 篇 neurons
86 篇 parallel process...
79 篇 deep neural netw...
74 篇 computer archite...
73 篇 distributed comp...
65 篇 signal processin...
62 篇 distributed proc...
62 篇 accuracy
60 篇 recurrent neural...
55 篇 data models
50 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 national laborat...
7 篇 beijing universi...
7 篇 natl univ def te...
6 篇 shanghai jiao to...
6 篇 the islamic univ...
6 篇 department of co...
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

2,925 篇 英文
72 篇 其他
8 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 3005 条记录，以下是1091-1100 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：

Prediction of the Cyanobacteria Coverage in Time-series Images based on Convolutional neural network 21

Prediction of the Cyanobacteria Coverage in Time-series Imag...

引用

4th International conference on Control and Computer Vision, ICCCV 2021

作者： Ye, Xiangyu Lai, Zhiquan Li, Dongsheng National Key Laboratory of Parallel and Distributed Processing Computer College National University of Defense Technology China

ISBN: (纸本)9781450390477

In recent years, the problem of lake eutrophication has become increasingly severe. The monitoring and control of cyanobacteria in lakes are of great significance. The information obtained by existing monitoring methods is relatively lagging, and it is impossible to monitor the sudden outbreak of cyanobacteria in time. Getting cyanobacteria information directly through camera images is a breakthrough. In this paper, after analyzing the characteristics of time series cyanobacteria images, we propose a block prediction scheme based on the CNN model. Experiments show that this method can quickly calculate the coverage of cyanobacteria in the monitoring image in a short time. It can also effectively distinguish cyanobacteria-rich water areas, which significantly facilitates water quality monitoring and cyanobacteria management. We can draw a chart of the changes in the coverage of cyanobacteria by analyzing multi-day time-series images. The chart helps us conduct a short-term water quality analysis to better deal with the outbreak of cyanobacteria. © 2021 ACM.

关键词： Lakes

来源：评论

学校读者我要写书评

暂无评论

SUBMODULAR RANK AGGREGATION ON SCORE-BASED PERMUTATIONS FOR distributed AUTOMATIC SPEECH RECOGNITION

SUBMODULAR RANK AGGREGATION ON SCORE-BASED PERMUTATIONS FOR ...

引用

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

作者： Qi, Jun Yang, Chao-Han Huck Tejedor, Javier Georgia Inst Technol Elect & Comp Engn Atlanta GA 30332 USA Univ San Pablo CEU CEU Univ Escuela Politecn Super Madrid Spain

ISBN: (纸本)9781509066315

distributed automatic speech recognition (ASR) requires to aggregate outputs of distributed deep neural network (DNN)-based models. This work studies the use of submodular functions to design a rank aggregation on score-based permutations, which can be used for distributed ASR systems in both supervised and unsupervised modes. Specifically, we compose an aggregation rank function based on the Lovasz Bregman divergence for setting up linear structured convex and nested structured concave functions. The algorithm is based on stochastic gradient descent (SGD) and can obtain well-trained aggregation models. Our experiments on the distributed ASR system show that the submodular rank aggregation can obtain higher speech recognition accuracy than traditional aggregation methods like Adaboost. Code is available online

关键词： distributed automatic speech recognition rank aggregation submodular function Lovasz Bregman divergence

来源：评论

学校读者我要写书评

暂无评论

CopGAT: Co-propagation Self-supervised Graph Attention network

CopGAT: Co-propagation Self-supervised Graph Attention Netwo...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Baoming Zhang Ming Xu Mingcai Chen Mingyuan Chen Chongjun Wang Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Graph Attention network (GAT) is one of the state-of-the-art architectures for Graph neural networks (GNNs). In this paper, we first propose Label Purity to explore the relationship between the graph attention and the node labels. By tracking the label purity of graph attention, we observe that graph attention suppresses message passing between inter-class nodes in the graph with homophily. Upon such basis, we further improve graph attention's capability to capture label information by designing a self-supervised graph attention loss. We propose a novel approach based on self-supervised graph attention loss, Co-propagation Self-supervised Graph Attention network (CopGAT), which explicitly restrains message passing between inter-class nodes. The Co-propagation means propagating node representations in Euclidean space and pseudo labels in probability simplex via the shared attention matrix, combining Message Passing neural networks and Label Propagation Algorithm under the graph attention framework. Constrained with the self-supervised graph attention loss generated by co-propagation, our CopGAT enhances the ability of GAT to attenuate the influence of structural noise. Furthermore, our self-supervised graph attention loss can be extended to other graph attention models in a plug-and-play manner. Experimental results demonstrate that our Cop GAT outperforms previous state-of-the-art methods on five benchmark datasets for the semi-supervised node classification task.

关键词： Message passing Benchmark testing Graph neural networks Task analysis

来源：评论

学校读者我要写书评

暂无评论

Detection of Melanoma Skin Cancer Using Capsule network and Multi-Task Learning Framework

Detection of Melanoma Skin Cancer Using Capsule Network and ...

引用

International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Esther Stacy E. B. Aggrey Qin Zhen Seth Larweh Kodjiku Kwame O. Asamoah Obed Barnes Linda Delali Fiasam Evan Aidoo Henrietta Aggrey School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China School of Computer and Information Engineering Zhejiang Gongshang University Hangzhou China College of Teacher Education Zhejiang Normal University Jinhua China Department of Computer Science and Information Technology University of Cape Coast Cape Coast Ghana Department of Mechanical Engineering Kwame Nkrumah University of Science & Technology Kumasi Ghana

ISBN: (纸本)9781665493901

Melanoma is the most dangerous and aggressive kind of skin cancer, which is also the most frequent form of cancer worldwide. Given the complexities involved, automatic melanoma detection using skin imaging has lately received interest within the machine learning field. Convolutional neural network has widely been employed in recent years to address this problem. However, existing CNN models for skin cancer classification have the drawback of ignoring crucial spatial relationship between features. They are only able to perform accurate classifications provided a predetermined set of features are present in the test data, regardless of how those features are distributed, which leads to false negatives. Furthermore, the CNN pooling layers responsible for down-sampling in these networks also result in loss of data and poor generalization performance. This study proposes a combination of convolutional block and Capsule neural network with a multi-task learning framework to address the aforementioned challenges and boost skin cancer classification. The model’s efficiency was measured by a number of metrics, including accuracy, specificity, recall, and F1 score. The accuracy of the proposed model achieved 98.93%, 98.52%, 95.7%, and 98.87%, respectively, indicating great efficiency when compared to other existing networks. As a result, the proposed method offers less sophisticated and robust architecture for automating the process of melanoma diagnoses and accelerating detection procedures in order to save a life.

关键词： Measurement neural networks Imaging Melanoma Machine learning Information processing Media

来源：评论

学校读者我要写书评

暂无评论

Object-Centric Learning with Slot Attention 34

Object-Centric Learning with Slot Attention

引用

34th conference on neural Information processing Systems (NeurIPS)

作者： Locatello, Francesco Weissenborn, Dirk Unterthiner, Thomas Mahendran, Aravindh Heigold, Georg Uszkoreit, Jakob Dosovitskiy, Alexey Kipf, Thomas Google Res Brain Team Mountain View CA 94043 USA Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Max Planck Inst Intelligent Syst Stuttgart Germany

ISBN: (纸本)9781713829546

Learning object-centric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from low-level perceptual features. Yet, most deep learning approaches learn distributed representations that do not capture the compositional properties of natural scenes. In this paper, we present the Slot Attention module, an architectural component that interfaces with perceptual representations such as the output of a convolutional neural network and produces a set of task-dependent abstract representations which we call slots. These slots are exchangeable and can bind to any object in the input by specializing through a competitive procedure over multiple rounds of attention. We empirically demonstrate that Slot Attention can extract object-centric representations that enable generalization to unseen compositions when trained on unsupervised object discovery and supervised property prediction tasks.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Training Transformers Together 35

Training Transformers Together

引用

35th Annual conference on neural Information processing Systems (NeurIPS)

作者： Borzunov, Alexander Ryabinin, Max Dettmers, Tim Lhoest, Quentin Saulnier, Lucile Diskin, Michael Jernite, Yacine Wolf, Thomas HSE Univ Yandex France Univ Washington Seattle WA USA Hugging Face New York NY USA

The infrastructure necessary for training state-of-the-art models is becoming overly expensive, which makes training such models affordable only to large corporations and institutions. Recent work proposes several methods for training such models collaboratively, i.e., by pooling together hardware from many independent parties and training a shared model over the Internet. In this demonstration, we collaboratively trained a text-to-image transformer similar to OpenAI DALL-E. We invited the viewers to join the ongoing training run, showing them instructions on how to contribute using the available hardware. We explained how to address the engineering challenges associated with such a training run (slow communication, limited memory, uneven performance between devices, and security concerns) and discussed how the viewers can set up collaborative training runs themselves. Finally, we show that the resulting model generates images of reasonable quality on a number of prompts.

关键词： distributed training volunteer computing transformers text-to-image memory efficiency communication efficiency heterogeneous hardware security

来源：评论

学校读者我要写书评

暂无评论

A Dynamic Mapping Model for General CNN Accelerator Based on FPGA 17th

A Dynamic Mapping Model for General CNN Accelerator Based on...

引用

17th IFIP WG 10.3 International conference on network and Parallel Computing, NPC 2020

作者： Zhao, Xiaoqiang Jiang, Jingfei Han, Zhe Xu, Jinwei Liu, Zhiqiang National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha China Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China

ISBN: (纸本)9783030794774

As the application scenarios of convolutional neural network (CNN) become more and more complex, the general CNN accelerator based on matrix multiplication has become a new research focus. The existing mapping methods for converting convolution calculation into matrix multiplication need to be improved. This paper proposes a new dynamic mapping model to improve the flexibility and versatility of matrix multiplication. The dynamic mapping model implements two algorithms: dynamic residue processing mapping algorithm (DRPMA) and dilated convolution mapping algorithm (DCMA). The former can dynamically adjust the mapping method according to the number of output channels of the convolution layer, improve the utilization of the multiply-accumulate (MAC) array. The latter extends the efficient support for Dilated CNNs. For demonstration, we implement an accelerator with Verilog on Xilinx VC709 FPGA board and test some typical CNN models. Experimental results show that the general accelerator achieves high performance and energy efficiency. © 2021, IFIP International Federation for Information processing.

关键词： Field programmable gate arrays (FPGA)

来源：评论

学校读者我要写书评

暂无评论

6th International conference on Advanced Intelligent Systems and Informatics, AISI 2020

6th International Conference on Advanced Intelligent Systems...

引用

6th International conference on Advanced Intelligent Systems and Informatics, AISI 2020

ISBN: (纸本)9783030586683

The proceedings contain 78 papers. The special focus in this conference is on Advanced Intelligent Systems and Informatics. The topics include: Deep neural networks for Landmines Images Classification;Deep Convolutional neural networks for ECG Heartbeat Classification Using Two-Stage Hierarchical Method;study of Region Convolutional neural network Deep Learning for Fire Accident Detection;norm-Referenced Achievement Grading: Methods and Comparison;review of Several Address Assignment Mechanisms for distributed Smart Meter Deployment in Smart Grid;an Approach for Sentiment Analysis and Personality Prediction Using Myers Briggs Type Indicator;article Reading Sequencing for English Terminology Learning in Professional Courses;egyptian Student Sentiment Analysis Using Word2vec During the Coronavirus (Covid-19) Pandemic;various Pre-processing Strategies for Domain-Based Sentiment Analysis of Unbalanced Large-Scale Reviews;an Enhanced Database Recovery Model Based on Game Theory for Mobile Applications;arabic Offline Character Recognition Model Using Non-dominated Rank Sorting Genetic Algorithm;sentiment Analysis of Hotel Reviews Using Machine Learning Techniques;transparent Blockchain-Based Voting System: Guide to Massive Deployments;Enhanced Technique for Detecting Active and Passive Black-Hole Attacks in MANET;a Secure Signature Scheme for IoT Blockchain Framework Based on Multimodal Biometrics;an Evolutionary Biometric Authentication Model for Finger Vein Patterns;a Deep Blockchain-Based Trusted Routing Scheme for Wireless Sensor networks;a Survey of Using Blockchain Aspects in Information Centric networks;real-Time Trajectory Control ofPotential Drug Carrier Using Pantograph "Experimental Study";Early Detection of COVID-19 Using a Non-contact Forehead Thermometer;Location Estimation of RF Emitting Source Using Supervised Machine Learning Technique;big Data Technology in Intelligent Distribution network: Demand and Applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient distributed Training 34

ScaleCom: Scalable Sparsified Gradient Compression for Commu...

引用

34th conference on neural Information processing Systems (NeurIPS)

作者： Chen, Chia-Yu Ni, Jiamin Lu, Songtao Cui, Xiaodong Chen, Pin-Yu Sun, Xiao Wang, Naigang Venkataramani, Swagath Srinivasan, Vijayalakshmi Zhang, Wei Gopalakrishnan, Kailash IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA

ISBN: (纸本)9781713829546

Large-scale distributed training of Deep neural networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained. To overcome this limitation, numerous gradient compression techniques have been proposed and have demonstrated high compression ratios. However, most existing methods do not scale well to large scale distributed systems (due to gradient build-up) and/or fail to evaluate model fidelity (test accuracy) on large datasets. To mitigate these issues, we propose a new compression technique, Scalable Sparsified Gradient Compression (ScaleCom), that leverages similarity in the gradient distribution amongst learners to provide significantly improved scalability. Using theoretical analysis, we show that ScaleCom provides favorable convergence guarantees and is compatible with gradient all-reduce techniques. Furthermore, we experimentally demonstrate that ScaleCom has small overheads, directly reduces gradient traffic and provides high compression rates (65-400X) and excellent scalability (up to 64 learners and 8-12X larger batch sizes over standard training) across a wide range of applications (image, language, and speech) without significant accuracy loss.

关键词： Scalability

来源：评论

学校读者我要写书评

暂无评论

Dynamic Path Based DNN Synergistic Inference Acceleration in Edge Computing Environment 27

Dynamic Path Based DNN Synergistic Inference Acceleration in...

引用

27th IEEE International conference on Parallel and distributed Systems, ICPADS 2021

作者： Zhou, Meng Zhou, Bowen Wang, Huitian Dong, Fang Zhao, Wei School of Computer Science and Engineering Southeast University Nanjing210000 China Institute for Artficial Intelligence Hefei Comprehensive National Science Center Hefei230000 China School of Computer Science and technology Anhui University of Technology Ma'anshan243000 China

ISBN: (纸本)9781665408783

Deep neural networks (DNNs) have achieved excellent performance in intelligent applications. Nevertheless, it is elusive for devices with limited resources to support computationally intensive DNNs, while employing the cloud may lead to prohibitive latency. Better solutions are exploiting edge computing and reducing unnecessary computation. Multi-exit DNN based on the early exit mechanism has an impressive effect in the latter, and in edge computing paradigm, model partition on multi-exit chain DNNs is proved to accelerate inference effectively. However, despite reducing computations to some extent, multiple exits may lead to instability of performance due to variable sample quality, performance inferior to the original model especially in the worst case. Furthermore, nowadays DNNs are universally characterized by a directed acyclic graph (DAG), complicating the partition of multi-exit DNN exceedingly. To solve the issues, in this paper, considering online exit prediction and model execution optimization for multi-exit DNN, we propose a Dynamic Path based DNN Synergistic inference acceleration framework (DPDS), where exit designators are designed to avoid iterative entry for exits;to further promote computational synergy in the edge, the multi-exit DNN is dynamically partitioned according to network environment to achieve fine-grained computing offloading. Experimental results show that DPDS can significantly accelerate DNN inference by 1.87× to 6.78×. © 2021 IEEE.

关键词： Deep 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301页 << < 106 107 108 109 110 111 112 113 114 115 > >>

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：