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

923 篇 期刊文献
619 篇 会议
12 篇 学位论文

馆藏范围

1,554 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,400 篇 工学
- 949 篇 计算机科学与技术...
- 492 篇 电气工程
- 179 篇 信息与通信工程
- 131 篇 控制科学与工程
- 129 篇 软件工程
- 76 篇 生物医学工程（可授...
- 73 篇 仪器科学与技术
- 60 篇 机械工程
- 49 篇 材料科学与工程（可...
- 37 篇 电子科学与技术（可...
- 32 篇 石油与天然气工程
- 27 篇 化学工程与技术
- 26 篇 测绘科学与技术
- 22 篇 动力工程及工程热...
- 22 篇 土木工程
- 22 篇 环境科学与工程（可...
- 18 篇 力学（可授工学、理...
- 17 篇 交通运输工程
- 14 篇 生物工程
334 篇 理学
- 126 篇 物理学
- 95 篇 生物学
- 62 篇 化学
- 53 篇 数学
- 37 篇 地球物理学
- 20 篇 统计学（可授理学、...
246 篇 医学
- 148 篇 临床医学
- 62 篇 特种医学
- 56 篇 基础医学(可授医学...
110 篇 管理学
- 89 篇 管理科学与工程(可...
- 15 篇 图书情报与档案管...
18 篇 农学
12 篇 经济学
10 篇 法学
10 篇 教育学
9 篇 文学
3 篇 艺术学

主题

1,554 篇 variational auto...
262 篇 deep learning
133 篇 anomaly detectio...
93 篇 machine learning
61 篇 generative adver...
47 篇 generative model
46 篇 training
43 篇 feature extracti...
42 篇 unsupervised lea...
39 篇 neural networks
37 篇 representation l...
36 篇 generative adver...
36 篇 data augmentatio...
34 篇 convolutional ne...
33 篇 data models
28 篇 artificial intel...
27 篇 semi-supervised ...
26 篇 collaborative fi...
26 篇 task analysis
26 篇 deep generative ...

机构

10 篇 natl chiao tung ...
6 篇 ucl england
6 篇 shenzhen univ co...
6 篇 shanghai univ sc...
5 篇 nanyang technol ...
5 篇 zhejiang lab peo...
5 篇 xiamen univ sch ...
5 篇 chung yuan chris...
4 篇 mit comp sci & a...
4 篇 univ chinese aca...
4 篇 beijing jiaotong...
4 篇 acad sinica res ...
4 篇 acad sinica taiw...
4 篇 oak ridge natl l...
4 篇 northwestern pol...
4 篇 ecole technol su...
4 篇 chongqing univ p...
4 篇 tsinghua univ de...
4 篇 univ elect sci &...
4 篇 zhejiang univ st...

作者

12 篇 chien jen-tzung
9 篇 tahan antoine
9 篇 zemouri ryad
8 篇 chen junghui
7 篇 yang fan
7 篇 utschick wolfgan...
7 篇 zhang hao
6 篇 tsao yu
6 篇 baur michael
6 篇 slavic giulia
6 篇 wang hsin-min
6 篇 regazzoni carlo
6 篇 marcenaro lucio
5 篇 guo rui
5 篇 li maokun
5 篇 hsu wei-ning
5 篇 glass james
5 篇 liu xin
5 篇 yoshii kazuyoshi
5 篇 li yan

语言

1,498 篇 英文
42 篇 其他
2 篇 中文
1 篇 朝鲜文
1 篇 土耳其文

检索条件"主题词=variational autoencoder"

共 1554 条记录，以下是1231-1240 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：

Towards speech enhancement using a variational U-Net architecture 29

Towards speech enhancement using a variational U-Net archite...

引用

29th European Signal Processing Conference (EUSIPCO)

作者： Nustede, Eike J. Anemueller, Joern Carl von Ossietzky Univ Oldenburg Computat Audit Grp Dept Med Phys & Acoust Oldenburg Germany Carl von Ossietzky Univ Oldenburg Computat Audit Grp Cluster Excellence Hearing4all Oldenburg Germany

ISBN: (纸本)9789082797060

We investigate the viability of a variational U-Net architecture for denoising of single-channel audio data. Deep network speech enhancement systems commonly aim to estimate filter masks, or opt to work on the waveform signal, potentially neglecting relationships across higher dimensional spectrotemporal features. We study the adoption of a probabilistic (variational) bottleneck into the classic U-Net architecture for direct spectral reconstruction. Evaluation of several ablation network variants is carried out using signal-to-distortion ratio and perceptual measures, on audio data that includes known and unknown noise types as well as reverberation. Our experiments show that the residual (skip) connections in the proposed system are a prerequisite for successful spectral reconstruction, i.e., without filter mask estimation. Results show, on average, an advantage of the proposed variational U-Net architecture over its classic, non-variational version in signal enhancement performance under reverberant conditions of 0.31 and 6.98 in PESQ and STOI scores, respectively. Anecdotal evidence points to improved suppression of impulsive noise sources with the variational U-Net compared to the recurrent mask estimation network baseline.

关键词： Speech enhancement U-Net architecture variational autoencoder deep learning audio source separation

来源：评论

学校读者我要写书评

暂无评论

Towards Source-Aligned variational Models for Cross-Domain Recommendation 21

Towards Source-Aligned Variational Models for Cross-Domain R...

引用

15th ACM Conference on Recommender Systems (RECSYS)

作者： Salah, Aghiles Tran, Thanh Binh Lauw, Hady W. Rakuten Inst Technol Paris France Singapore Management Univ Singapore Singapore

ISBN: (纸本)9781450384582

Data sparsity is a long-standing challenge in recommender systems. Among existing approaches to alleviate this problem, cross-domain recommendation consists in leveraging knowledge from a source domain or category (e.g., Movies) to improve item recommendation in a target domain (e.g., Books). In this work, we advocate a probabilistic approach to cross-domain recommendation and rely on variational autoencoders (VAEs) as our latent variable models. More precisely, we assume that we have access to a VAE trained on the source domain that we seek to leverage to improve preference modeling in the target domain. To this end, we propose a model which learns to fit the target observations and align its hidden space with the source latent space jointly. Since we model the latent spaces by the variational posteriors, we operate at this level, and in particular, we investigate two approaches, namely rigid and soft alignments. In the former scenario, the variational model in the target domain is set equal to the source variational model. That is, we only learn a generative model in the target domain. In the soft-alignment scenario, the target VAE has its variational model, but which is encouraged to look like its source counterpart. We analyze the proposed objectives theoretically and conduct extensive experiments to illustrate the benefit of our contribution. Empirical results on six real-world datasets show that the proposed models outperform several comparable cross-domain recommendation models.

关键词： Cross-Domain Recommendation variational autoencoder Collaborative Filtering Neural Networks

来源：评论

学校读者我要写书评

暂无评论

IDA-GAN: A Novel Imbalanced Data Augmentation GAN 25

IDA-GAN: A Novel Imbalanced Data Augmentation GAN

引用

25th International Conference on Pattern Recognition (ICPR)

作者： Yang, Hao Zhou, Yun Natl Univ Def Technol Sci & Technol Informat Syst Engn Lab Changsha Hunan Peoples R China

ISBN: (纸本)9781728188089

Class imbalance is a widely existed and challenging problem in real-world applications such as disease diagnosis, fraud detection, network intrusion detection and so on. Due to the scarce of data, it could significantly deteriorate the accuracy of classification. To address this challenge, we propose a novel Imbalanced Data Augmentation Generative Adversarial Networks (GAN) named IDA-GAN as an augmentation tool to deal with the imbalanced dataset. This is a great challenge because it is hard to train a GAN model under this situation. We address this issue by coupling variational autoencoder along with GAN training. In this paper, specifically, we introduce the variational autoencoder to learn the majority and minority class distributions in the latent space, and use the generative model to utilize each class distribution for the subsequent GAN training. The generative model learns useful features to generate target minority-class samples. Compared with the state-of-the-art GAN model, the experimental results demonstrate that our proposed IDA-GAN could generate more diverse minority samples with better qualities, and it could benefits the imbalanced classification task in terms of several widely-used evaluation metrics on five benchmark datasets: MNIST, Fashion-MNIST, SVHN, CIFAR-10 and GTSRB.

关键词： Imbalanced learning Data augmentation variational autoencoder GAN

来源：评论

学校读者我要写书评

暂无评论

Controllable Gradient Item Retrieval 21

Controllable Gradient Item Retrieval

引用

30th World Wide Web Conference (WWW)

作者： Wang, Haonan Zhou, Chang Yang, Carl Yang, Hongxia He, Jingrui Univ Illinois Urbana IL USA Alibaba Grp Hangzhou Peoples R China Emory Univ Atlanta GA 30322 USA

ISBN: (纸本)9781450383127

In this paper, we identify and study an important problem of gradient item retrieval. We define the problem as retrieving a sequence of items with a gradual change on a certain attribute, given a reference item and a modification text. For example, after a customer saw a white dress, she/he wants to buy a similar one but more floral on it. The extent of "more floral" is subjective, thus prompting one floral dress is hard to satisfy the customer's needs. A better way is to present a sequence of products with increasingly floral attributes based on the white dress, and allow the customer to select the most satisfactory one from the sequence. Existing item retrieval methods mainly focus on whether the target items appear at the top of the retrieved sequence, but ignore the demand for retrieving a sequence of products with gradual change on a certain attribute. To deal with this problem, we propose a weakly-supervised method that can learn a disentangled item representation from user-item interaction data and ground the semantic meaning of attributes to dimensions of the item representation. Our method takes a reference item and a modification as a query. During inference, we start from the reference item and "walk" along the direction of the modification in the item representation space to retrieve a sequence of items in a gradient manner. We demonstrate our proposed method can achieve disentanglement through weak supervision. Besides, we empirically show that an item sequence retrieved by our method is gradually changed on an indicated attribute and, in the item retrieval task, our method outperforms existing approaches on three different datasets.

关键词： information retrieval recommendation system weakly-supervised learning disentangled representation learning variational autoencoder

来源：评论

学校读者我要写书评

暂无评论

Open-Set Audio Classification with Limited Training Resources based on Augmentation Enhanced variational Auto-Encoder GAN with Detection-Classification Joint Training 22

Open-Set Audio Classification with Limited Training Resource...

引用

Interspeech Conference

作者： Kah Kuan Teh Huy Dat Tran ASTAR Inst Infocomm Res Aural & Language Intelligence Dept Singapore Singapore

ISBN: (纸本)9781713836902

In this paper, we propose a novel method to address practical problems when deploying audio classification systems in operations that are the presence of unseen sound classes (open-set) and the limitation of training resources. To solve it, a novel method which embeds variational auto-encoder (VAE), data augmentation and detection-classification joint training into conventional GAN networks is proposed. The VAE input to GAN-generator helps to generate realistic outlier samples which are not too far from in-distribution class and hence improve the open-set discrimination capabilities of classifiers. Next, the augmentation enhanced GAN scheme developed in our previous work [4] for close-set audio classification, will help to address the limited training resources by in cooperating the physical data augmentation to work together with traditional GAN produced samples to prevent overfitting and improve the optimization convergences. The detection-classification joint training further steps on advantages of VAE and Augmentation GAN to further improving the performances of detection and classification tasks. The experiments carried out on Google Speech Command database show great improvements of open-set classification accuracy from 62.41% to 88.29% when using only 10% amount of training data.

关键词： Audio Classification Limited Training variational autoencoder Generative Adversarial Networks Open set classification Sinkhorn divergence

来源：评论

学校读者我要写书评

暂无评论

Learning Disentangled Representations with the Wasserstein autoencoder 1

引用

21st Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Gaujac, Benoit Feige, Ilya Barber, David UCL London England

ISBN: (数字)9783030865238

ISBN: (纸本)9783030865238;9783030865221

Disentangled representation learning has undoubtedly benefited from objective function surgery. However, a delicate balancing act of tuning is still required in order to trade off reconstruction fidelity versus disentanglement. Building on previous successes of penalizing the total correlation in the latent variables, we propose TCWAE (Total Correlation Wasserstein autoencoder). Working in the WAE paradigm naturally enables the separation of the total-correlation term, thus providing disentanglement control over the learned representation, while offering more flexibility in the choice of reconstruction cost. We propose two variants using different KL estimators and analyse in turn the impact of having different ground cost functions and latent regularization terms. Extensive quantitative comparisons on data sets with known generative factors shows that our methods present competitive results relative to state-of-the-art techniques. We further study the trade off between disentanglement and reconstruction on more-difficult data sets with unknown generative factors, where the flexibility of the WAE paradigm leads to improved reconstructions.

关键词： Wasserstein autoencoder variational autoencoder Generative modelling Representation learning Disentanglement learning

来源：评论

学校读者我要写书评

暂无评论

P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model 37

P3GM: Private High-Dimensional Data Release via Privacy Pres...

引用

37th IEEE International Conference on Data Engineering (IEEE ICDE)

作者： Takagi, Shun Takahashi, Tsubasa Cao, Yang Yoshikawa, Masatoshi Kyoto Univ Kyoto Japan LINE Corp Toronto ON Canada

ISBN: (纸本)9781728191843

How can we release a massive volume of sensitive data while mitigating privacy risks? Privacy-preserving data synthesis enables the data holder to outsource analytical tasks to an untrusted third party. The state-of-the-art approach for this problem is to build a generative model under differential privacy, which offers a rigorous privacy guarantee. However, the existing method cannot adequately handle high dimensional data. In particular, when the input dataset contains a large number of features, the existing techniques require injecting a prohibitive amount of noise to satisfy differential privacy, which results in the outsourced data analysis meaningless. To address the above issue, this paper proposes privacy-preserving phased generative model (P3GM), which is a differentially private generative model for releasing such sensitive data. P3GM employs the two-phase learning process to make it robust against the noise, and to increase learning efficiency (e.g., easy to converge). We give theoretical analyses about the learning complexity and privacy loss in P3GM. We further experimentally evaluate our proposed method and demonstrate that P3GM significantly outperforms existing solutions. Compared with the state-of-the-art methods, our generated samples look fewer noises and closer to the original data in terms of data diversity. Besides, in several data mining tasks with synthesized data, our model outperforms the competitors in terms of accuracy.

关键词： differential privacy variational autoencoder generative model privacy preserving data synthesis

来源：评论

学校读者我要写书评

暂无评论

Classification and Authentication of Mineral Water Samples using Electronic Tongue and Deep Neural Networks 3

Classification and Authentication of Mineral Water Samples u...

引用

3rd IEEE International Conference on Cognitive Machine Intelligence (IEEE CogMI)

作者： Damarla, Seshu Kumar Zhu, Xiuli Kundu, Madhusree Univ Alberta Dept Chem & Mat Engn Edmonton AB Canada Donghua Univ Dept Informat Sci & Technol Shanghai Peoples R China Natl Inst Technol Rourkela Dept Chem Engn Rourkela Odisha India

ISBN: (纸本)9781665416214

Supervised multiclass classifiers based on deep neural networks (one-dimensional convolutional neural network (1D-CNN) and long short-term network (LSTM)) are developed to classify and authenticate mineral water samples of commercial brands (Aquafina, Bisleri, Oasis, Kingfisher, Dolphin and McDowell) available in Indian market. Electronic tongue based experiments are conducted to generate output waveforms (current signals) of the water samples of the six brands. Since the data obtained via the experiments are not adequate to train the deep neural networks, variational autoencoder (VAE) is used to generate additional current signals for each of the six brands. New database consisting of the experimental data and data generated using VAE is utilized to train and test the classifiers. Classification accuracies obtained by the LSTM and 1D-CNN models are 83.33% and 66.66%, respectively.

关键词： electronic tongue long short-term network convolutional neural network variational autoencoder multiclass classifier

来源：评论

学校读者我要写书评

暂无评论

Computational Intelligence Techniques Applied to a Preliminary Exploration of Climatic Traits using Meteosat Water Vapor Images

Computational Intelligence Techniques Applied to a Prelimina...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Valdes, Julio J. Pou, Antonio Natl Res Council Canada Digital Technol Res Ctr Ottawa ON Canada Autonomous Univ Madrid Dept Ecol Madrid Spain

ISBN: (纸本)9780738133669

Meteorological satellites are crucial for understanding and forecasting the flow dynamics of the General Atmospheric Circulation System. They produce large amounts of image information whose analysis and interpretation poses numerous challenges. A collection of computational intelligence techniques are used for investigating the structure of a large series of water vapor (WV) band images from ESA Meteosat satellites from 2009-2020. They include the use of the Visual Information Fidelity image quality measure, intrinsic dimensionality, Uniform Manifold Approximation and Projection (with density information) and deep learning (variational autoencoders, convolutional neural networks and transfer learning). The variational autoencoder extracted features that characterize Water Vapor dynamics, distinguished different season patterns, exposed their interrelationships and provided highly effective classification features. Dimensionality reduction was highly instrumental applied to the VAE's latent spaces, and to VIFp image disimilarity analysis in a novel approach applied to major geographical areas. Supervised classification with deep learning and with the XGBoost algorithm on VAE's latent space, produced highly accurate models for predicting seasons. This computational intelligence approach provided insights about atmospheric dynamics as seen from the WV band images and suggested possible weather-teleconnection processes, as well as new ways for finding signs of unusual climate behavior.

关键词： computational intelligence water vapor satellite images VIFp image similarity intrinsic dimension Umap dimensionality reduction variational autoencoder convolutional neural networks transfer learning atmosphere dynamics climate variations

来源：评论

学校读者我要写书评

暂无评论

Fast Multi-Step Critiquing for VAE-based Recommender Systems 21

Fast Multi-Step Critiquing for VAE-based Recommender Systems

引用

15th ACM Conference on Recommender Systems (RECSYS)

作者： Antognini, Diego Faltings, Boi Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781450384582

Recent studies have shown that providing personalized explanations alongside recommendations increases trust and perceived quality. Furthermore, it gives users an opportunity to refine the recommendations by critiquing parts of the explanations. On one hand, current recommender systems model the recommendation, explanation, and critiquing objectives jointly, but this creates an inherent trade-off between their respective performance. On the other hand, although recent latent linear critiquing approaches are built upon an existing recommender system, they suffer from computational inefficiency at inference due to the objective optimized at each conversation's turn. We address these deficiencies with M&Ms-VAE, a novel variational autoencoder for recommendation and explanation that is based on multimodal modeling assumptions. We train the model under a weak supervision scheme to simulate both fully and partially observed variables. Then, we leverage the generalization ability of a trained M&Ms-VAE model to embed the user preference and the critique separately. Our work's most important innovation is our critiquing module, which is built upon and trained in a self-supervised manner with a simple ranking objective. Experiments on four real-world datasets demonstrate that among state-of-the-art models, our system is the first to dominate or match the performance in terms of recommendation, explanation, and multi-step critiquing. Moreover, M&Ms-VAE processes the critiques up to 25.6x faster than the best baselines. Finally, we show that our model infers coherent joint and cross generation, even under weak supervision, thanks to our multimodal-based modeling and training scheme.

关键词： Conversational Recommendation Critiquing variational autoencoder

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156页 << < 120 121 122 123 124 125 126 127 128 129 > >>

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：