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

69 篇 期刊文献
39 篇 会议
1 篇 学位论文

馆藏范围

109 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

101 篇 工学
- 63 篇 计算机科学与技术...
- 33 篇 电气工程
- 13 篇 信息与通信工程
- 9 篇 控制科学与工程
- 9 篇 软件工程
- 6 篇 仪器科学与技术
- 4 篇 电子科学与技术（可...
- 3 篇 机械工程
- 3 篇 化学工程与技术
- 3 篇 生物医学工程（可授...
- 2 篇 测绘科学与技术
- 2 篇 航空宇航科学与技...
- 2 篇 环境科学与工程（可...
- 2 篇 生物工程
28 篇 理学
- 10 篇 生物学
- 7 篇 物理学
- 7 篇 地球物理学
- 6 篇 数学
- 5 篇 化学
- 3 篇 大气科学
- 2 篇 天文学
- 2 篇 统计学（可授理学、...
- 1 篇 地理学
- 1 篇 地质学
- 1 篇 系统科学
- 1 篇 生态学
16 篇 医学
- 8 篇 临床医学
- 3 篇 基础医学(可授医学...
- 2 篇 特种医学
7 篇 管理学
- 6 篇 管理科学与工程(可...
2 篇 农学

主题

109 篇 adversarial auto...
16 篇 deep learning
14 篇 anomaly detectio...
8 篇 generative adver...
8 篇 unsupervised lea...
7 篇 feature extracti...
6 篇 variational auto...
6 篇 machine learning
5 篇 generative adver...
4 篇 semi-supervised ...
4 篇 human activity r...
3 篇 ground reaction ...
3 篇 synthetic apertu...
3 篇 time series
3 篇 gan
2 篇 clustering
2 篇 drug discovery
2 篇 inertial measure...
2 篇 generative model
2 篇 synthetic data g...

机构

3 篇 china univ geosc...
2 篇 chongqing normal...
2 篇 zhejiang univ co...
2 篇 gifu univ fac en...
2 篇 istanbul tech un...
2 篇 ludwig maximilia...
2 篇 kyoto univ grad ...
2 篇 aselsan inc anka...
1 篇 wenzhou univ col...
1 篇 univ michigan de...
1 篇 nanjing univ pos...
1 篇 hong kong polyte...
1 篇 shandong univ ke...
1 篇 fudan univ sch c...
1 篇 univ waterloo wa...
1 篇 univ vienna fac ...
1 篇 key lab comp vir...
1 篇 middlesex univ e...
1 篇 south china univ...
1 篇 yonsei univ grad...

作者

3 篇 hernandez vincen...
3 篇 venture gentiane
3 篇 zuo renguang
2 篇 zhang fuzhi
2 篇 yaslan yusuf
2 篇 tuzen ahmet
2 篇 goto keisuke
2 篇 yan hao
2 篇 zhang chunjie
2 篇 aizawa hiroaki
2 篇 saito takaho
2 篇 xiong yihui
2 篇 zhou jinglin
2 篇 kato kunihito
2 篇 yeh huai-ming
2 篇 li mingyong
2 篇 cao zeyu
1 篇 li yonghui
1 篇 meng fanli
1 篇 zhang tao

语言

107 篇 英文
2 篇 其他

检索条件"主题词=adversarial autoencoder"

共 109 条记录，以下是81-90 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：

De Novo Molecular Generation with Stacked adversarial Model 34th

De Novo Molecular Generation with Stacked Adversarial Model

引用

34th Australasian Joint Conference on Artificial Intelligence (AI)

作者： Liu, Yuansan Bailey, James Univ Melbourne Melbourne Vic Australia

ISBN: (纸本)9783030975463;9783030975456

Generating novel drug molecules with desired biological properties is a time consuming and complex task. Conditional generative adversarial models have recently been proposed as promising approaches for de novo drug design. In this paper, we propose a new generative model which extends an existing adversarial autoencoder (AAE) based model by stacking two models together. Our stacked approach generates more valid molecules, as well as molecules that are more similar to known drugs. We break down this challenging task into two sub-problems. A first stage model to learn primitive features from the molecules and gene expression data. A second stage model then takes these features to learn properties of the molecules and refine more valid molecules. Experiments and comparison to baseline methods on the LINCS L1000 dataset demonstrate that our proposed model has promising performance for molecular generation.

关键词： De novo Molecule generation adversarial autoencoder Stacking

来源：评论

学校读者我要写书评

暂无评论

Spectrum-Guided adversarial Disparity Learning 20

Spectrum-Guided Adversarial Disparity Learning

引用

26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Liu, Zhe Yao, Lina Bai, Lei Wang, Xianzhi Wang, Can Univ New South Wales Sydney NSW Australia Univ Technol Sydney Sydney NSW Australia Griffith Univ Nathan Qld Australia

ISBN: (纸本)9781450379984

It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between subject-specific variation for each activity class. In this work, we propose a novel end-toend knowledge directed adversarial learning framework, which portrays the class-conditioned intraclass disparity using two competitive encoding distributions and learns the purified latent codes by denoising learned disparity. Furthermore, the domain knowledge is incorporated in an unsupervised manner to guide the optimization and further boosts the performance. The experiments on four HAR benchmark datasets demonstrate the robustness and generalization of our proposed methods over a set of state-of-the-art. We further prove the effectiveness of automatic domain knowledge incorporation in performance enhancement.

关键词： Intraclass variability adversarial autoencoder activity recognition generative models

来源：评论

学校读者我要写书评

暂无评论

Analysis of the B5G/6G Communication Power Entropy Patterns Based on Generative AI Methods 3

Analysis of the B5G/6G Communication Power Entropy Patterns ...

引用

3rd Conference on Information Technology and Data Science

作者： Talbi, Djamila Gal, Zoltan Univ Debrecen Fac Informat Debrecen Hungary

ISBN: (纸本)9798350387896;9798350387889

Moving toward new and higher frequencies would bring the 6G communication network into practice. Using a new MAC mechanism will enhance and overcome the THz challenges. Our paper focused on analyzing the entropy interdependence between two subsystems, normal and lucky mobile terminals. The two introduced entropy metrics play a crucial role in interpreting the interdependence of the two subsystems. However, the lack of data obliged us to use GAN and its methods to generate similar data. Therefore, we used four different GAN methods plus the standard GAN to obtain the most significant data, and we determined the similarity using six different similarity metrics. The results showed that cosine and correlation similarities are not appropriate to capture the similarity, meanwhile, the rest;dynamic time warping, Frechet inception, root mean square error, and peak signal-to-noise ratio agreed that DCGAN was the one who generated the most accurate data series compared to the rest.

关键词： 6G B5G Entropy MAC Mechanism Generative adversarial Networks adversarial autoencoder Deep Convolutional GAN Least Squares GAN Wasserstein GAN

来源：评论

学校读者我要写书评

暂无评论

Spammer Group Detection Approach Based on Deep Reinforcement Learning 20th

Spammer Group Detection Approach Based on Deep Reinforcement...

引用

20th International Conference on Intelligent Computing (ICIC)

作者： Huo, Chenghang Cui, Jindong Ma, Ru Luo, Yunfei Zhang, Fuzhi Yanshan Univ Sch Informat Sci & Engn Qinhuangdao Hebei Peoples R China

ISBN: (纸本)9789819756056;9789819756063

Detecting spammer groups is important for maintaining the normal operation of e-commerce platforms. Nevertheless, current spammer group detection methods ignore the overlapping between spammer groups. Moreover, hand-crafted indicators-based methods lack universality. Aiming at these problems, we propose a deep reinforcement learning-based spammer group detection approach. First, we model the review dataset as a user-item bipartite graph that serves as the interaction environment of the agent, and utilize the BiNE model to get the initial user vector representations. Then, we model the generation of candidate groups in the bipartite graph as the Markov decision process and introduce the self-attention mechanism to aggregate node features. Meanwhile, we use a deep Q-network to obtain the overlapped candidate group sequences. Finally, we apply an adversarial autoencoder to detect spammer groups. Experiments on Yelp Miami, Yelp New York, YelpCHI and Amazon datasets reveal that our method performs better than three baselines.

关键词： Spammer Group Detection Deep Q-network adversarial autoencoder Self-attention Mechanism

来源：评论

学校读者我要写书评

暂无评论

Sketch-aae: A Seq2Seq Model to Generate Sketch Drawings 3

Sketch-aae: A Seq2Seq Model to Generate Sketch Drawings

引用

3rd International Conference on Vision, Image and Signal Processing (ICVISP)

作者： Lu, Jia Li, Xueming Zhang, Xianlin Beijing Univ Posts & Telecommun Sch Digital Media & Design Arts Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Key Lab Network Syst & Network Culture Beijing Peoples R China Beijing Univ Posts & Telecommun Dept Informat & Commun Engn Beijing Peoples R China

ISBN: (纸本)9781450376259

Sketch plays an important role in human nonverbal communication, which is a superior way to describe specific objects visually. Generating human free-hand sketches has become topical in computer graphics and vision, inspired by various applications related to sketches such as sketch object recognition. Existing methods on sketch generation failed to utilize stroke sequence information of human free-hand sketches. Especially, a recent study proposed an end-to-end variational autoencoder (VAE) model called sketch-rnn which learned to sketch with human input. However, the performance of sketch-rnn is affected by the original input seriously hence decreased its robustness. In this paper, we proposed a sequence-to-sequence model called sketch-aae to generate multiple categories of human-like sketches of higher quality than sketch-rnn. We achieve this by introducing an adversarial autoencoder (AAE) model, which uses generative adversarial networks (GAN) to improve the robustness of VAE. To our best knowledge, for the first time, the AAE model is used to synthesize sketches. A VGGNet classification model is then formulated to prove the similarity between our generated sketches and human free-hand sketches. Extensive experiments both qualitatively and quantitatively demonstrate that the proposed model is superiority over the state-of-the-art for sketch generation and multi-class sketch classification.

关键词： Sketch Generation VAE GAN adversarial autoencoder

来源：评论

学校读者我要写书评

暂无评论

adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores 23

Adversarial Anomaly Detection using Gaussian Priors and Nonl...

引用

23rd IEEE International Conference on Data Mining (IEEE ICDM)

作者： Lueer, Fiete Weber, Tobias Dolgich, Maxim Boehm, Christian eMundo Gmbh Gofore Oyj Munich Germany Ludwig Maximilians Univ Munchen Dept Stat Munich Germany Univ Vienna Fac Comp Sci Vienna Austria

ISBN: (纸本)9798350381641

Anomaly detection in imbalanced datasets is a frequent and crucial problem, especially in the medical domain where retrieving and labeling irregularities is often expensive. By combining the generative stability of a beta-variational autoencoder (VAE) with the discriminative strengths of generative adversarial networks (GANs), we propose a novel model, beta-VAEGAN. We investigate methods for composing anomaly scores based on the discriminative and reconstructive capabilities of our model. Existing work focuses on linear combinations of these components to determine if data is anomalous. We advance existing work by training a kernelized support vector machine (SVM) on the respective error components to also consider nonlinear relationships. This improves anomaly detection performance, while allowing faster optimization. Lastly, we use the deviations from the Gaussian prior of beta-VAEGAN to form a novel anomaly score component. In comparison to state-of-the-art work, we improve the F-1 score during anomaly detection from 0.85 to 0.92 on the widely used MITBIH Arrhythmia Database.(1)

关键词： adversarial autoencoder generative adversarial networks variational autoencoder anomaly detection time series

来源：评论

学校读者我要写书评

暂无评论

Fashion Coordinates Recommendation based on User Behavior and Visual Clothing Style 17

Fashion Coordinates Recommendation based on User Behavior an...

引用

3rd International Conference on Communication and Information Processing (ICCIP)

作者： Gu, Sida Liu, Xiaoqiang Cai, Lizhi Shen, Jie Donghua Univ Coll Comp Sci & Technol 2999 North Renmin Rd Shanghai 200030 Peoples R China Shanghai Key Lab Comp Software Testing & Evaluat 1588 Lianhang Rd Shanghai 201112 Peoples R China Univ Michigan Dept Comp & Informat Sci 4901 Evergreen Rd Dearborn MI 48128 USA

ISBN: (纸本)9781450353656

Fashion coordinates recommendation focuses on coordinating the pattern and colors of fashion items. For example, which shirt out of the candidates matches the pants best? The traditional recommender system based on Latent Factor Model (LFM) uses user behavior features to deal with the recommendation of two matching item, but it is weak in recommending more than two items because the problem of cold start becomes significant. Furthermore, fashion coordinates are correlated with visual features of items directly, which is another factor that can be imported into the recommendation calculation. In this paper, we propose a fashion coordinates system which considers both user behaviors and visual fashion styles. We extend LFM to dealing with user behavior features, and a deep learning model called Denoising autoencoder is used to process visual features. Combining those two methods, our system can recommend multi-items more accurate than traditional methods and support cold start. Initial experiments on coordinating three kinds of items (tops, trousers and shoes) are demonstrated in detail.

关键词： Recommender system fashion coordinates adversarial autoencoder Latent Factor Model

来源：评论

学校读者我要写书评

暂无评论

DP-VAE: Human-Readable Text Anonymization for Online Reviews with Differentially Private Variational autoencoders 22

DP-VAE: Human-Readable Text Anonymization for Online Reviews...

引用

31st ACM Web Conference (WWW)

作者： Weggenmann, Benjamin Rublack, Valentin Andrejczuk, Michael Mattern, Justus Kerschbaum, Florian SAP Secur Res Karlsruhe Germany Univ Edinburgh Edinburgh Scotland Univ Waterloo Waterloo ON Canada

ISBN: (纸本)9781450390965

While vast amounts of personal data are shared daily on public online platforms and used by companies and analysts to gain valuable insights, privacy concerns are also on the rise: Modern authorship attribution techniques have proven effective at identifying individuals from their data, such as their writing style or behavior of picking and judging movies. It is hence crucial to develop data sanitization methods that allow sharing of users' data while protecting their privacy and preserving quality and content of the original data. In this paper, we tackle anonymization of textual data and propose an end-to-end differentially private variational autoencoder architecture. Unlike previous approaches that achieve differential privacy on a per-word level through individual perturbations, our solution works at an abstract level by perturbing the latent vectors that provide a global summary of the input texts. Decoding an obfuscated latent vector thus not only allows our model to produce coherent, high-quality output text that is human-readable, but also results in strong anonymization due to the diversity of the produced data. We evaluate our approach on IMDb movie and Yelp business reviews, confirming its anonymization capabilities and preservation of the semantics and utility of the original sentences.

关键词： differential privacy text anonymization variational autoencoder adversarial autoencoder disentangled representations authorship obfuscation authorship attribution online reviews

来源：评论

学校读者我要写书评

暂无评论

Generative Networks Applied to Model Fluid Flows 22nd

Generative Networks Applied to Model Fluid Flows

引用

22nd Annual International Conference on Computational Science (ICCS)

作者： Jolaade, Mustapha Silva, Vinicius L. S. Heaney, Claire E. Pain, Christopher C. Imperial Coll London Appl Modelling & Computat Grp London England

ISBN: (纸本)9783031087578;9783031087561

The production of numerous high fidelity simulations has been a key aspect of research for many-query problems in fluid dynamics. The computational resources and time required to generate these simulations can be so large and impractical. With several successes of generative models, we explore the performance and powerful generative capabilities of both generative adversarial network (GAN) and adversarial autoencoder (AAE) to predict the evolution in time of a highly nonlinear fluid flow. These generative models are incorporated within a reduced-order model framework. The test case comprises two-dimensional Gaussian vortices governed by the time-dependent Navier-Stokes equation. We show that both the GAN and AAE are able to predict the evolution of the positions of the vortices forward in time, generating new samples that have never before been seen by the neural networks.

关键词： Generative adversarial networks adversarial autoencoder Two-dimensional turbulence Spatial-temporal predictions Deep learning

来源：评论

学校读者我要写书评

暂无评论

Image Embedding for Detecting Irregularity 3rd

Image Embedding for Detecting Irregularity

引用

3rd International Conference on Computer Vision and Image Processing (CVIP)

作者： Sharma, M. K. Sheet, D. Biswas, Prabir Kumar IIT Kharagpur Adv Technol Dev Ctr Kharagpur W Bengal India IIT Kharagpur Elect Engn Kharagpur W Bengal India IIT Kharagpur Elect & Elect Commun Engn Kharagpur W Bengal India

ISBN: (纸本)9789813292918;9789813292901

Detecting irregularity in an image or video is an important task in quality control or automatic visual inspection. This paper presents an image embedding technique for detecting an irregularity or abnormality in images. This can further be utilized in image screening application. In the proposed architecture, deep adversarial autoencoder is trained to extract the features from images. Using these features and skip-gram model, we develop the image2vec architecture to capture contextual probability in an image. Various score aggregation techniques are explored and its performance is reported. As a case study, we present a scenario of foreign body object detection in clinical-grade X-ray images. The proposed approach is found to correctly detect and localize abnormality in images.

关键词： Visual inspection Image embedding Contextual abnormality detection adversarial autoencoder Skip-gram Negative sampling

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11页 << < 2 3 4 5 6 7 8 9 10 11 > >>

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：