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检索条件"主题词=variational autoencoder"

共 1537 条记录，以下是661-670 订阅

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Regularizing variational autoencoders for Molecular Graph Generation 1

引用

26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Li, Xin Lyu, Xiaoqing Zhang, Hao Hu, Keqi Tang, Zhi Peking Univ Beijing Peoples R China Beijing Inst Technol Beijing Peoples R China China Univ Min & Technol Beijing Peoples R China

ISBN: (数字)9783030368029

ISBN: (纸本)9783030368029;9783030368012

Deep generative models for graphs are promising for being able to sidestep expensive search procedures in the huge space of chemical compounds. However, incorporating complex and non-differentiable property metrics into a generative model remains a challenge. In this work, we formulate a differentiable objective to regularize a variational autoencoder model that we design for graphs. Experiments demonstrate that the regularization performs excellently when used for generating molecules since it can not only improve the performance of objectives optimization task but also generate molecules with high quality in terms of validity and novelty.

关键词： variational autoencoder Regularization Molecule generation Graph generation

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DISENTANGLEMENT LEARNING FOR variational autoencoderS APPLIED TO AUDIO-VISUAL SPEECH ENHANCEMENT

DISENTANGLEMENT LEARNING FOR VARIATIONAL AUTOENCODERS APPLIE...

引用

IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

作者： Carbajal, Guillaume Richter, Julius Gerkmann, Timo Univ Hamburg Signal Proc SP Hamburg Germany

ISBN: (纸本)9781665448703

Recently, the standard variational autoencoder has been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. variational autoencoders have then been conditioned on a label describing a high-level speech attribute (e.g. speech activity) that allows for a more explicit control of speech generation. However, the label is not guaranteed to be disentangled from the other latent variables, which results in limited performance improvements compared to the standard variational autoencoder. In this work, we propose to use an adversarial training scheme for variational autoencoders to disentangle the label from the other latent variables. At training, we use a discriminator that competes with the encoder of the variational autoencoder. Simultaneously, we also use an additional encoder that estimates the label for the decoder of the variational autoencoder, which proves to be crucial to learn disentanglement. We show the benefit of the proposed disentanglement learning when a voice activity label, estimated from visual data, is used for speech enhancement.

关键词： Speech enhancement conditional generative model variational autoencoder disentanglement learning adversarial training semi-supervised learning audio-visual.

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Empirical Evaluation of variational autoencoders for Data Augmentation 13

Empirical Evaluation of Variational Autoencoders for Data Au...

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13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Jorge, Javier Vieco, Jesus Paredes, Roberto Andreu Sanchez, Joan Miguel Benedi, Jose Univ Politecn Valencia Dept Sistemas Informat & Computat Valencia Spain

ISBN: (纸本)9789897583063

Since the beginning of Neural Networks, different mechanisms have been required to provide a sufficient number of examples to avoid overfitting. Data augmentation, the most common one, is focused on the generation of new instances performing different distortions in the real samples. Usually, these transformations are problem-dependent, and they result in a synthetic set of, likely, unseen examples. In this work, we have studied a generative model, based on the paradigm of encoder-decoder, that works directly in the data space, that is, with images. This model encodes the input in a latent space where different transformations will be applied. After completing this, we can reconstruct the latent vectors to get new samples. We have analysed various procedures according to the distortions that we could carry out, as well as the effectiveness of this process to improve the accuracy of different classification systems. To do this, we could use both the latent space and the original space after reconstructing the altered version of these vectors. Our results have shown that using this pipeline (encoding-altering-decoding) helps the generalisation of the classifiers that have been selected.

关键词： Generative Models Data Augmentation variational autoencoder

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Wake-Sleep variational autoencoders for Language Modeling 24th

Wake-Sleep Variational Autoencoders for Language Modeling

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24th International Conference on Neural Information Processing (ICONIP)

作者： Shen, Xiaoyu Su, Hui Niu, Shuzi Klakow, Dietrich Saarland Univ Spoken Language Syst LSV Saarbrucken Germany Univ Chinese Acad Sci Software Inst Beijing Peoples R China

ISBN: (纸本)9783319700878;9783319700861

variational autoencoders (VAEs) are known to easily suffer from the KL-vanishing problem when combining with powerful autoregressive models like recurrent neural networks (RNNs), which prohibits their wide application in natural language processing. In this paper, we tackle this problem by tearing the training procedure into two steps: learning effective mechanisms to encode and decode discrete tokens (wake step) and generalizing meaningful latent variables by reconstructing dreamed encodings (sleep step). The training pattern is similar to the wake-sleep algorithm: these two steps are trained alternatively until an equilibrium is achieved. We test our model in a language modeling task. The results demonstrate significant improvement over the current state-of-the-art latent variable models.

关键词： variational autoencoder Wake-sleep algorithm Language modeling Latent variable

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Expanding variational autoencoders for learning and exploiting latent representations in search distributions 18

Expanding variational autoencoders for learning and exploiti...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Garciarena, Unai Santana, Roberto Mendiburu, Alexander Univ Basque Country Intelligent Syst Grp Donostia San Sebastian Spain

ISBN: (纸本)9781450356183

In the past, evolutionary algorithms (EAs) that use probabilistic modeling of the best solutions incorporated latent or hidden variables to the models as a more accurate way to represent the search distributions. Recently, a number of neural-network models that compute approximations of posterior (latent variable) distributions have been introduced. In this paper, we investigate the use of the variational autoencoder (VAE), a class of neural-network based generative model, for modeling and sampling search distributions as part of an estimation of distribution algorithm. We show that VAE can capture dependencies between decision variables and objectives. This feature is proven to improve the sampling capacity of model based EAs. Furthermore, we extend the original VAE model by adding a new, fitness-approximating network component. We show that it is possible to adapt the architecture of these models and we present evidence of how to extend VAEs to better fulfill the requirements of probabilistic modeling in EAs. While our results are not yet competitive with state of the art probabilistic-based optimizers, they represent a promising direction for the application of generative models within EDAs.

关键词： machine learning variational autoencoder estimation of distribution algorithm neural network generative modeling

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ENSEMBLE OF UNSUPERVISED LEARNED IMAGE REPRESENTATIONS BASED ON variational autoencoderS FOR LUNG ADENOCARCINOMA SUBTYPE DIFFERENTIATION 20

ENSEMBLE OF UNSUPERVISED LEARNED IMAGE REPRESENTATIONS BASED...

引用

20th IEEE International Symposium on Biomedical Imaging (ISBI)

作者： Cano, Fabian Alvarez-Jimenez, Charlems Romero, Eduardo Cruz-Roa, Angel Univ Nacl Colombia Cim Lab Res Grp Bogota Colombia Univ Llanos GITECX Villavicencio Colombia Univ Llanos Adalab Res Grp Villavicencio Colombia

ISBN: (纸本)9781665473583

Lung adenocarcinoma is a type of non-small cell lung cancer that accounts for about 40% of all lung cancers, which is divided into different molecular and histological subtypes associated with particular prognosis and treatment. Pathologists stratify for diagnosis mainly by its histo-morphological visual features and patterns, which tends to be challenging because of the nature of lung tissue, a mixture of histologically complex patterns and not having a specialized grading system. Here, an unsupervised computational approach based on an ensemble of tissue-specialized variational auto-encoders, which were trained per histopathology subtype, to build an unsupervised embedded tissue-image representation. This representation was used to train a Random Forest classifier of three lung adenocarcinoma histology subtypes (lepidic, papillary and solid), and a 2D-visually interpretable projection from the learned embedded representation. Experimental results achieve an average F-score of 0.72 +/- 0.05 in the test dataset and a well-separated 2D visual mapping of tissue subtypes.

关键词： Digital Pathology Classification Tissue Representation variational autoencoder Lung Adenocarcinoma

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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...

引用

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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