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检索条件"主题词=variational autoencoder"

共 1532 条记录，以下是661-670 订阅

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Providing Previously Unseen Users Fair Recommendations Using variational autoencoders 23

Providing Previously Unseen Users Fair Recommendations Using...

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17th ACM Conference on Recommender Systems (RecSys)

作者： Vassoy, Bjornar Langseth, Helge Kille, Benjamin Norwegian Univ Sci & Technol Trondheim Trondelag Norway

ISBN: (纸本)9798400702419

An emerging definition of fairness in machine learning requires that models are oblivious to demographic user information, e.g., a user's gender or age should not influence the model. Personalized recommender systems are particularly prone to violating this definition through their explicit user focus and user modelling. Explicit user modelling is also an aspect that makes many recommender systems incapable of providing hitherto unseen users with recommendations. We propose novel approaches for mitigating discrimination in variational autoencoder-based recommender systems by limiting the encoding of demographic information. The approaches are capable of, and evaluated on, providing users that are not represented in the training data with fair recommendations.

关键词： fairness variational autoencoder

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Synthetic Electrochemical Biosensor Data with Conditional variational autoencoders for Enhanced Predictive Modeling

Synthetic Electrochemical Biosensor Data with Conditional Va...

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2024 IEEE Sensors Conference

作者： Teo, Desmond Kai Xiang Maul, Tomas Tan, Michelle Tien Tien Univ Nottingham Malaysia Dept Elect & Elect Engn Semenyih Selangor Malaysia Univ Nottingham Malaysia Sch Comp Sci Semenyih Selangor Malaysia Univ Nottingham Malaysia Nanotechnol Res Grp Semenyih Selangor Malaysia

ISBN: (纸本)9798350363524;9798350363517

Electrochemical biosensors (EB) have revolutionized various domains with their rapid and sensitive analyte detection capabilities. However, as EBs advance to tackle more challenging targets, the complexity of post hoc analysis intensifies, posing a significant bottleneck. Integrating AI with biosensing devices offers a promising solution, yet widespread adoption faces hurdles such as limited data availability. This study introduces a novel approach leveraging Conditional variational autoencoders to generate synthetic electrochemical data for augmenting training datasets. This approach aims to address the specific issue of data scarcity in the post hoc analysis of electrochemical biosensing data. Evaluation experiments demonstrate the effectiveness of incorporating synthetic data in enhancing prediction model performance, reducing the MRE by 41.16%, and achieving similar performance to a model trained with the full dataset by using only 50% of the training data supplemented with synthetic data. This work highlights a critical advancement, bridging the gap between AI and electrochemical biosensing, ultimately improving the efficiency and accuracy of post hoc analysis in this vital field.

关键词： Artificial Neural Network Voltammetry Electrochemical Biosensor Multilayer Perceptron Synthetic Data Generation variational autoencoder

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Digital pathology whole slide image compression with Vector Quantized variational autoencoders

Digital pathology whole slide image compression with Vector ...

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Conference on Medical Imaging - Digital and Computational Pathology

作者： Keighley, Jason de Kamps, Marc Wright, Alexander Treanor, Darren Univ Leeds Woodhouse Lane Leeds LS2 9JT W Yorkshire England Leeds Teaching Hosp NHS Trust Great George St Leeds LS1 3EX W Yorkshire England Linkoping Univ Dept Clin Pathol Linkoping Sweden Linkoping Univ Dept Clin & Expt Med Linkoping Sweden Linkoping Univ Ctr Med Image Sci & Visualizat CMIV Linkoping Sweden Univ Leeds LIDA Leeds W Yorkshire England Alan Turing Inst 96 Euston Rd London NW1 2DB England

ISBN: (纸本)9781510660472;9781510660489

Digital pathology Whole Slide Images (WSIs) are large images (similar to 30 GB/slide uncompressed) of high resolution (0.25 microns per pixel), presenting a significant data storage challenge for hospitals wishing to adopt digital pathology. Lossy compression has been adopted by scanner manufacturers to address this issue - we compare lossy Joint Photographic Experts Group (JPEG) compression for WSIs and investigate the Vector Quantised variational autoencoder 2 variant (VQVAE2) as a possible alternative to reduce file size while encoding useful features in the compressed representation. We trained three VQVAE2 models on a Camelyon 2016 subset to the Compression Ratio (CR) of 19.2:1 (CR1), 9.6:1 (CR2) and 4.8:1 (CR3) and tested on a Camelyon 2016 (DS1) subset;University of California (DS2) and Internal Validation Set (DS3). We then compared compression performance to ImageMagick JPEG and JPEG 2000 implementations. Both JPEG and JPEG 2000 compression outperformed the VQVAE2 implementation within the Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics. The trained VQVAE2 models could visually reproduce WSI tissue structure, but used colours from the original training data within the reconstructions on other datasets.

关键词： variational autoencoder Quantisation Compression Pathology Whole Slide Images Digital Pathology

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

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

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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

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Interpreting variational autoencoders with Fuzzy Logic: A step towards interpretable deep learning based fuzzy classifiers

Interpreting Variational Autoencoders with Fuzzy Logic: A st...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Bolat, Kutay Kumbasar, Tufan Istanbul Tech Univ Dept Control & Automat Engn Istanbul Turkey

ISBN: (纸本)9781728169323

The emerging success of Deep Learning (DL) in various application areas comes also with the questions starting with "How"s and "Why"s. These questions can be answered if the DL methods are interpretable and thus provide a certain a degree of explanation. In this paper, we propose a DL framework that leverages the advantages of beta-variational autoencoder (VAE) and Fuzzy Sets (FSs), which are disentanglement and linguistic representation, for the design of a novel DL based Fuzzy Classifier (FC). We first present a step-by-step design approach to construct the DL-FC which is composed of the encoder layer of beta-VAE and a Fuzzy Logic System (FLS) followed by a softmax layer. The beta-VAE is trained so that the semantic information of the high dimensional data is captured. The latent space of the beta-VAE is clustered to extract FSs. The FSs are then used to define antecedents of the FLS that is trained with DL methods. We present results conducted on the MNIST dataset and showed that DL-FC is quite competitive with its deep neural network counterpart. We then try to provide an interpretation to the antecedents of FLS by examining the FSs, the latent traversals and heat-maps of each latent dimension. The results show that the antecedents of FLS can be defined with linguistic interpretations. Thus, for the first time in the literature, we showed that linguistic interpretations can be defined for the latent space of beta-VAE with FSs.

关键词： variational autoencoder fuzzy sets fuzzy c-means clustering classification interpretation

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Sequential Segment-based Level Generation and Blending using variational autoencoders 20

Sequential Segment-based Level Generation and Blending using...

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15th International Conference on the Foundations of Digital Games

作者： Sarkar, Anurag Cooper, Seth Northeastern Univ Boston MA 02115 USA

ISBN: (纸本)9781450388078

Existing methods of level generation using latent variable models such as VAEs and GANs do so in segments and produce the final level by stitching these separately generated segments together. In this paper, we build on these methods by training VAEs to learn a sequential model of segment generation such that generated segments logically follow from prior segments. By further combining the VAE with a classifier that determines whether to place the generated segment to the top, bottom, left or right of the previous segment, we obtain a pipeline that enables the generation of arbitrarily long levels that progress in any of these four directions and are composed of segments that logically follow one another. In addition to generating more coherent levels of non-fixed length, this method also enables implicit blending of levels from separate games that do not have similar orientation. We demonstrate our approach using levels from Super Mario Bros., Kid Icarus and Mega Man, showing that our method produces levels that are more coherent than previous latent variable-based approaches and are capable of blending levels across games.

关键词： procedural content generation variational autoencoder level generation level blending game blending PCGML

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Data Augmentation and Recognition for Plunger Lift Based on variational autoencoders 6

Data Augmentation and Recognition for Plunger Lift Based on ...

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6th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2024

作者： Xu, Zhiyu Lin, Hai Jia, Yujiang Du, Jing Mao, Jianjun Zhu, Jianjun Chen, Jun Li, Fangming Li, Nan Petro China Qinghai Oilfield Dunhuang China China University of Petroleum Beijing China Research Institute of Petroleum Exploration & Development Beijing China

ISBN: (纸本)9798331516550

Plunger lift technology effectively addresses liquid loading issues in the later stages of natural gas well production. With the continuous development of artificial intelligence technology, the rapid identification of plunger data has become a challenge in field operations. Due to the lack of labeled field data and significant data imbalance, it is difficult to directly identify using discriminative models. This paper proposes a plunger lift generative model based on variational autoencoders (VAEs) for data augmentation of pressure data in plunger lift processes. The model maps original data through an encoder to obtain a high-dimensional latent variable probability distribution;the decoder maps the latent variable back to the real space to generate samples. The generated data supplements the original data, which is then input into a discriminative network for training. The results show that compared to the original discriminative model, the model's performance indicators have improved after data augmentation. © 2024 IEEE.

关键词： Liquid loading Plunger lift variational autoencoder

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Differentially Private Synthetic Data Generation via Lipschitz-Regularised variational autoencoders 9

Differentially Private Synthetic Data Generation via Lipschi...

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9th IEEE Smart World Congress, SWC 2023

作者： Gros, Benedikt Wunder, Gerhard Freie Universität Department of Computer Science Berlin Germany

ISBN: (纸本)9798350319804

Synthetic data has been hailed as the silver bullet for privacy preserving data analysis. If a record is not real, then how could it violate a person's privacy? In addition, deep-learning based generative models are employed successfully to approximate complex high-dimensional distributions from data and draw realistic samples from this learned distribution. It is often overlooked though that generative models are prone to memorising many details of individual training records and often generate synthetic data that too closely resembles the underlying sensitive training data, hence violating strong privacy regulations as, e.g., encountered in health care. Differential privacy is the well-known state-of-the-art framework for guaranteeing protection of sensitive individuals' data, allowing aggregate statistics and even machine learning models to be released publicly without compromising privacy. The training mechanisms however often add too much noise during the training process, and thus severely compromise the utility of these private models. Even worse, the tight privacy budgets do not allow for many training epochs so that model quality cannot be properly controlled in practice. In this paper we explore an alternative approach for privately generating data that makes direct use of the inherent stochasticity in generative models, e.g., variational autoencoders. The main idea is to appropriately constrain the continuity modulus of the deep models instead of adding another noise mechanism on top. For this approach, we derive mathematically rigorous privacy guarantees and illustrate its effectiveness with practical experiments. © 2023 IEEE.

关键词： differential privacy Lipschitz regularisation variational autoencoder

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Learning Physically Meaningful Representations of Energy Systems with variational autoencoders 27

Learning Physically Meaningful Representations of Energy Sys...

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IEEE 27th International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Multaheb, Samim Bauer, Fabian Bretschneider, Peter Niggemann, Oliver Helmut Schmidt Univ Inst Automat Technol Hamburg Germany Ilmenau Univ Technol Inst Elect Power & Control Engn Ilmenau Germany

ISBN: (数字)9781665499965

ISBN: (纸本)9781665499965

Given the growing number of volatile energy producers and consumers and the limitations of traditional static load prediction models, we have analyzed the ability of neural networks to predict the loads of an electrical transformer and to understand the physical relationships in the electric grid. To do this, we use a variational autoencoder to learn the load behavior of a neighborhood with three houses. Since a variational autoencoder learns a latent representation, we analyzed the possibility of learning physical relationships of the electric grid. By adapting the prediction model to learn a physical variable, namely phase shift, we show that a variational autoencoder can learn physical relations. Our results show a significant improvement in terms of the correlation of the latent and physical variables by integrating prior knowledge in the form of the corresponding power values as the training objective.

关键词： Representation Learning Neural Networks variational autoencoder Explainable Artificial Intelligence Informed Machine Learning

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TRANSFER LEARNING FROM SYNTHETIC TO REAL IMAGES USING variational autoencoderS FOR PRECISE POSITION DETECTION 25

TRANSFER LEARNING FROM SYNTHETIC TO REAL IMAGES USING VARIAT...

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25th IEEE International Conference on Image Processing (ICIP)

作者： Inoue, Tadanobu Chaudhury, Subhajit De Magistris, Giovanni Dasgupta, Sakyasingha IBM Res Tokyo Japan Ascent Robot Inc Tokyo Japan

ISBN: (纸本)9781479970612

Capturing and labeling camera images in the real world is an expensive task, whereas synthesizing labeled images in a simulation environment is easy for collecting large-scale image data. However, learning from only synthetic images may not achieve the desired performance in the real world due to a gap between synthetic and real images. We propose a method that transfers learned detection of an object position from a simulation environment to the real world. This method uses only a significantly limited dataset of real images while leveraging a large dataset of synthetic images using variational autoencoders. Additionally, the proposed method consistently performed well in different lighting conditions, in the presence of other distractor objects, and on different backgrounds. Experimental results showed that it achieved accuracy of 1.5 mm to 3.5 mm on average. Furthermore, we showed how the method can be used in a real-world scenario like a "pick-and-place" robotic task.

关键词： deep learning position detection transfer learning variational autoencoder computer simulation

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