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检索条件"主题词=Variational autoencoder"

共 1537 条记录，以下是361-370 订阅

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Designing Novel Functional Peptides by Manipulating a Temperature in the Softmax Function Coupled with variational autoencoder

Designing Novel Functional Peptides by Manipulating a Temper...

引用

IEEE International Conference on Big Data (Big Data)

作者： Chen, Shuan Kim, Hyun Uk Korea Adv Inst Sci & Technol KAIST Dept Chem & Biomol Engn Daejeon South Korea Korea Adv Inst Sci & Technol KAIST Dept Chem & Biomol Engn KAIST Inst Artificial Intelligence Daejeon South Korea

ISBN: (纸本)9781728108582

Development of an efficient peptide design method is crucial for tackling medical problems, such as designing antimicrobial peptides for combating drug resistant pathogens and anticancer peptides for various cancers. Here, we present variational autoencoder (VAE) coupled with a Softmax function having a temperature factor (1) for high-throughput design of novel functional peptides. VAE is a generative machine learning model, which has proved to be useful for generating peptide sequences. In this study, we additionally use a Softmax function with T to facilitate determining the most probable amino acids at each position of peptide sequences to be generated, which is difficult to achieve using a conventional VAE. In particular, by manipulating T in the Softmax function, we select biologically most feasible peptides with a desired function. This method is demonstrated for designing novel antimicrobial and anticancer peptides in this study. The method presented herein should be useful for designing various peptides with a desired function upon availability of relevant datasets.

关键词： peptide design Softmax function with a temperature factor variational autoencoder

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Unsupervised Domain Adaptation via variational autoencoder with Explicit and Implicit Feature Alignment for Cardiac Segmentation 8

Unsupervised Domain Adaptation via Variational Autoencoder w...

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8th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)

作者： Li, Yan Wang, Yifan Zheng, Fan Cheng, Linkai Xu, Di Cui, Hengfei Northwestern Polytech Univ Sch Comp Sci Xian Peoples R China Huawei Cloud Media Innovat Lab Xian Peoples R China Northwestern Polytech Univ Shenzhen Inst Res & Dev Shenzhen Peoples R China

ISBN: (纸本)9781665455336

Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) is of vital importance in the diagnosis of myocardial infarction (MI). However, the LGE image annotations are limited, it is urgently needed to transfer the learned knowledge in other modalities. We propose an unsupervised domain adaptation (UDA) algorithm based on variational autoencoder (VAE) with a Combination of Explicit and Implicit Alignment of feature distributions (VCEIA). First, we re-derive the Kullback-Leibler (KL) divergence of the features extracted in the source and target domains as a measure of distribution discrepancy. Secondly, for explicit category-level distribution alignment, we use contrast learning to reduce the distance between prototypes of the same category in the source and target domains. Finally, we add a domain discriminator to the output space to implicitly align the feature distributions. Extensive experiments were conducted to validate the effectiveness of VCEIA on the MS-CMRSeg 2019 dataset, which showed competitive results.

关键词： cardiac segmentation domain adaptation distribution alignment variational autoencoder

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Website Recommendation with Side Information Aided variational autoencoder 39

Website Recommendation with Side Information Aided Variation...

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39th IEEE International Performance Computing and Communications Conference (IPCCC)

作者： Wang, Pinhao Li, Wenzhong Yu, Zepeng Lu, Baoguo Lu, Sanglu Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Sino German Inst Social Comp Nanjing Peoples R China Nanjing Res Inst Elect & Engn Nanjing Peoples R China

ISBN: (纸本)9781728198293

Recommender systems had been proposed to help people to find the interested items, such as recommending products to a buyer;identifying movies or music that a user will find interest, etc. However, the existing recommendation approaches mainly focus on capturing user-item interaction patterns for prediction, and ignore the user's side information such as visit frequency and duration. In this paper, we study the side information aided website recommendation problem that using the browsing history of a set of users and their side information to predict the websites that will be of interest to a certain user. We propose a novel recommendation approach called SI-VAE that incorporates side information with the variational autoencoders (VAEs) model for top-k recommendation. The proposed method takes both user-website interaction information and side information as input, and adopts an encoder/decoder model to generate user's interested websites from partial observations. The model of SI-VAE is implemented as a neural network, and trained with a multinomial likelihood objective function to form the ranking of user-website interaction probabilities. We conduct extensive experiments on two real-world datasets, which show that the proposed model outperforms the baselines in a number of performance metrics in website recommendation.

关键词： variational autoencoder website recommendation side information collaborative filtering

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CCVAE: A variational autoencoder for Handling Censored Covariates 21

CCVAE: A Variational Autoencoder for Handling Censored Covar...

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21st IEEE International Conference on Machine Learning and Applications (IEEE ICMLA)

作者： Svahn, Caroline Sysoev, Oleg Linkoping Univ Dept Comp & Informat Sci Linkoping Sweden Ericsson Res Gothenburg Sweden

ISBN: (纸本)9781665462839

For time or safety critical scenarios when faulty predictions or decisions can have crucial consequences, such as in certain telecommunications scenarios, reliable prediction models and accurate data are of the essence. When modeling and predicting data in such scenarios, data with censored covariates remain an issue as ignoring them or imputing them with lack of precision may cause inaccurate or uncertain predictions. In this paper, we provide a fast, reliable variational autoencoder framework which can handle covariate censoring in high dimensional data. Our numerical experiments demonstrate that our framework compares favorably to alternative methods in terms of prediction accuracy for both the response and the covariates, while enabling estimation of the prediction uncertainties. We moreover demonstrate that the method is at least 8 times faster than the benchmark models used in this paper, and more robust to initial imputations and noise than existing models. The method can also be used directly for predicting unseen data, which is a challenge for some state-of-the-art methods.

关键词： variational autoencoder censored covariates zero inflated truncated normal deep learning dynamic range

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PARAN: variational autoencoder-based End-to-End Articulation-to-Speech System for Speech Intelligibility 25

PARAN: Variational Autoencoder-based End-to-End Articulation...

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25th Interspeech Conference

作者： Um, Seyun Kim, Doyeon Kang, Hong-Goo Yonsei Univ Dept Elect & Elect Engn Seoul South Korea

Deep learning-based articulation-to-speech (ATS) systems designed for individuals with speech disorders have been extensively researched in recent years. However, conventional methods have faced challenges in representing the transformation in latent space across speech and electromagnetic articulography (EMA) domains, resulting in low speech quality. In this paper, we propose a variational autoencoder (VAE)-based end-to-end ATS model called PARAN that efficiently produces high-fidelity speech from EMA signals. Our model adjusts a prior distribution of latent representations from EMA signals to match a posterior distribution derived from speech utilizing a normalizing flow mechanism. To further enhance the clarity and intelligibility of the synthesized speech, we incorporate an additional loss function aimed at predicting phonetic information from EMA signals. Experimental results demonstrate that our model outperforms previous methods in terms of speech quality and intelligibility.(1)

关键词： Articulation-to-speech synthesis variational autoencoder electromagnetic articulography

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Efficient Side-Channel Attack through Balanced Labels Compression and variational autoencoder 19

Efficient Side-Channel Attack through Balanced Labels Compre...

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19th International Conference on Mobility, Sensing and Networking (MSN)

作者： Cai, Nengfu Yang, Zhigin Wang, Shuhai Jiang, Yanling Liu, Mingsheng Li, Gang Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Shijiazhuang Railway Univ Sch Informat Sci & Technol Shijiazhuang 050043 Peoples R China Shijiazhuang Inst Railway Technol Shijiazhuang 050041 Peoples R China Zhongke Zidong Informat Technol Beijing Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350358261;9798350358278

Recently, side-channel attacks based on deep learning (DLSCAs) have attracted much attention. Many works have improved the performance of DLSCAs by designing advanced neural network architectures and training strategies. There are few studies on leakage models for DLSCAs. Existing researches usually utilize the intermediate value Hamming weight (HW) and the intermediate value itself (ID) as leakage models. Training a classifier with good performance is challenging due to the many label classes in the ID leakage model. The HW leakage model can significantly reduce the number of labels, but it will cause samples imbalance. In this paper, we propose a new DLSCA leakage model, named Balanced Labels Compression (BLC). We consider dividing sensitive intermediate values with same lowest epsilon bits into same class to obtain balanced labels. Then, we train a classifier using the compressed BLC labels and profiling energy traces. At the attack phase, the probability distribution of BLC labels is extended to the probability distribution of sensitive intermediate values. We conduct extensive comparison experiments with HW, ID, and BLC leakage models under the two scenarios of sufficient and insufficient profiling energy traces. Further, we exploit VAE to improve attack performance when energy traces are insufficient. Experimental results show that VAE-based data augmentation can significantly reduce required energy traces.

关键词： Side-channel attacks Side-channel leakage model variational autoencoder Data augmentation

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Identifying Outliers, Defective Areas and Performance Evaluation of a PV Module using EL Image Processing: A variational autoencoder Approach

Identifying Outliers, Defective Areas and Performance Evalua...

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2023 Innovations in Power and Advanced Computing Technologies, i-PACT 2023

作者： Soumik, Moontasir Azmi, Taium Tasneem Islam, Mohammad Aminul Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Mechanical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9798350325188

Enhancing the commercial viability and efficacy of PV solar cell usage necessitates a comprehensive investigation into fault identification. This study introduces the development of a defect detection system tailored for identifying solar cell defects while approximating the subsequent power and efficiency losses. The methodology proposed encompasses two primary phases: Firstly, an anomaly detection model utilizing the variational Auto Encoder identifies defects, reconstructs defect-free solar cells or modules, and generates a heatmap illustrating defect intensities. Subsequently, in the second phase, the defective area is quantified to compute total power and efficiency losses of a PV module. The method's outcomes, utilizing Electroluminescence (EL) images, machine learning, and defect simulation, demonstrate its capability to operate with minimal available data and estimate overall power and efficiency loss. This research establishes a correlation between defects and power generation loss, offering an inclusive estimation of PV module profitability, potentially mitigating the soft costs of PV systems and enhancing fault diagnosis methods. © 2023 IEEE.

关键词： and module Anomaly Detection Image Processing Power Loss Estimation PV Cell variational autoencoder

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Facial Image Inpainting with variational autoencoder

Facial Image Inpainting with Variational Autoencoder

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2nd International Conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Tu, Ching-Ting Chen, Yi-Fu Natl Chung Hsing Univ Dept Appl Math Taichung 402 Taiwan Tamkang Univ Dept Comp Sci & Informat Engn New Taipei Taiwan

ISBN: (纸本)9781728141923

This paper proposed a learning-based approach to reveal diversity possible appearances under the missing area of an occluded unseen image. In general, there are a lot of possible facial appearances for the missing area;for example, a male with a scarf, it is difficult to predict he has a beard in the covered area or not? In this paper, we propose a novel method for facial image inpainting, which generates the missing facial appearance by conditioning on the observable appearance. Given a trained standard variational autoencoder (VAE) for un-occluded face generation. To be specified, we search for the possible set of VAE coding vector for the current occluded input image, and the predicted coding should be robust to the missing area. The possible facial appearance set is then recovered through the decoder of VAE model. Experiments show that our method successfully predicts recovered results in large missing regions;these results are diverse, and all are reasonable to be consistent with the observable facial area, i.e., both the facial geometry and the personal characteristics are preserved.

关键词： image inpainting variational autoencoder sampling

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Initial Study of Batik Generation using variational autoencoder 8

Initial Study of Batik Generation using Variational Autoenco...

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8th International Conference on Computer Science and Computational Intelligence, ICCSCI 2023

作者： Ihsan, Aditya Firman School of Computing Telkom University Bandung40257 Indonesia

One of the most promising architectures for generative models is the variational autoencoder (VAE). To reconstruct Batik patterns for this work, we used a deep convolutional VAE architecture. Reconstruction outcomes from various batik motifs are mapped and contrasted using some criteria. As another crucial component of the convolutional network, batch normalization's impact on the model's performance was also examined. The dataset is used to study some learned latent space features. Through these findings, we laid the framework for next research on Batik generation utilizing VAE. © 2023 The Authors. Published by Elsevier B.V.

关键词： Batik convolutional neural network generative model variational autoencoder

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Design-Thinking-Driven variational autoencoder for Human Activity Classification 2

Design-Thinking-Driven Variational Autoencoder for Human Act...

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Selvakumar, N. Kalaivani, D. Vijayaraghavan, T. Department of Computer science Dr.SNS Rajalakshmi College of arts and science Coimbatore India

ISBN: (纸本)9798350340235

This study introduces a design-thinking-driven approach aimed at enhancing the accuracy and robustness of Human Activity Recognition (HAR) using sensor data from Internet of Things (IoT) devices. The proposed algorithm harnesses the synergy of a variational autoencoder-Based Dimensionality Reduction Technique with deep learning methods for human activity classification. HAR plays a pivotal role in categorizing activities, especially in domains like elder care and fitness tracking. This research work systematically assesses the model's performance across diverse datasets, including WISDM, HAPT, HAR, and KU-HAR, utilizing performance evaluation metrics such as accuracy, recall, precision, and F1-Score. The results underscore the potential of this design-thinking-infused approach to significantly enhance the precision and reliability of activity prediction in IoT-based HAR applications. The study introduces a design-thinking-driven approach by combining variational autoencoder-Based Dimensionality Reduction with deep learning techniques for enhancing Human Activity Recognition (HAR). © 2023 IEEE.

关键词： Deep Learning Design Thinking Dimensionality Reduction Human Activity Recognition Internet of Things Sensor Data variational autoencoder

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