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IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： Wang, Zhenxing Wang, Yadong Harbin Inst Technol Sch Comp Sci & Technol Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781538654880

Lung cancer causes over one million deaths each year worldwide. DNA methylation is a well-defined epigenetics factor in genome data analyses for model training. In this article, we explore the applications of unsupervised deep learning method, variational autoencoders, using DNA methylation data of lung cancer samples downloaded from the GDC TCGA project and perform further work with latent features. We show the logistic regression classifier on the encoded latent features accurately classifies cancer subtypes.

关键词： DNA methylation lung cancer variational autoencoder

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Towards Evaluating the Representation Learned by variational autoencoders 60

Towards Evaluating the Representation Learned by Variational...

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60th Annual Conference of the Society-of-Instrument-and-Control-Engineers-of-Japan (SICE)

作者： Ueda, Tatsuya Vargas, Danilo Vasconcellos Kyushu Univ Grad Sch Informat Sci & Elect Engn Fukuoka Japan Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka Japan

ISBN: (纸本)9784907764739

At the heart of a deep neural network is representation learning with complex latent variables. This representation learning has been improved by disentangled representations and the idea of regularization terms. However, adversarial samples show that tasks with DNNs can easily fail due to slight perturbations or transformations of the input. variational autoencoder (VAE) learns P(zjx), the distribution of the latent variable z, rather than P(yjx), the distribution of the output y for the input x. Therefore, VAE is considered to be a good model for learning representations from input data. In other words, the mapping of x is not directly to y, but to the latent variable z. In this paper, we propose an evaluation method to characterize the latent variables that VAE learns. Specifically, latent variables extracted from VAEs trained by two well-known data sets are analyzed by the k-nearest neighbor method(kNN). In doing so, we propose an interpretation of what kind of representation the VAE learns, and share clues about the hyperdimensional space to which the latent variables are mapped.

关键词： variational autoencoder representation learning deep neural network

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Interpolation of missing swaption volatility data using variational autoencoders

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Behaviormetrika 2024年第1期51卷 291-317页

作者： Richert, Ivo Buch, Robert Department of Financial Mathematics Fraunhofer ITWM Kaiserslautern Germany

Albeit of crucial interest for financial researchers, market-implied volatility data of European swaptions often exhibit large portions of missing quotes due to illiquidity of the underlying swaption instruments. In this case, standard stochastic interpolation tools like the common SABR model cannot be calibrated to observed volatility smiles, due to data being only available for the at-the-money quote of the respective underlying swaption. Here, we propose to infer the geometry of the full unknown implied volatility cube by learning stochastic latent representations of implied volatility cubes via variational autoencoders, enabling inference about the missing volatility data conditional on the observed data by an approximate Gibbs sampling approach. Up to our knowledge, our studies constitute the first-ever completely nonparametric approach to modeling swaption volatility using unsupervised learning methods while simultaneously tackling the issue of missing data. Since training data for the employed variational autoencoder model is usually sparsely available, we propose a novel method to generate synthetic swaption volatility data for training and afterwards test the robustness of our approach on real market quotes. In particular, we show that SABR interpolated volatilities calibrated to reconstructed volatility cubes with artificially imputed missing values differ by not much more than two basis points compared to SABR fits calibrated to the complete cube. Moreover, we demonstrate how the imputation can be used to successfully set up delta-neutral portfolios for hedging purposes. © 2023, The Author(s).

关键词： Gibbs sampling Missing data imputation Swaption variational autoencoder

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Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders 23rd

Extracting a biologically relevant latent space from cancer ...

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23rd Pacific Symposium on Biocomputing (PSB)

作者： Way, Gregory P. Greene, Casey S. Univ Penn Genom & Computat Biol Grad Program Philadelphia PA 19104 USA Univ Penn Dept Syst Pharmacol & Translat Therapeut Philadelphia PA 19104 USA

ISBN: (纸本)9789813235533;9789813235526

The Cancer Genome Atlas (TCGA) has profiled over 10,000 tumors across 33 different cancer-types for many genomic features, including gene expression levels. Gene expression measurements capture substantial information about the state of each tumor. Certain classes of deep neural network models are capable of learning a meaningful latent space. Such a latent space could be used to explore and generate hypothetical gene expression profiles under various types of molecular and genetic perturbation. For example, one might wish to use such a model to predict a tumor's response to specific therapies or to characterize complex gene expression activations existing in differential proportions in different tumors. variational autoencoders (VAEs) are a deep neural network approach capable of generating meaningful latent spaces for image and text data. In this work, we sought to determine the extent to which a VAE can be trained to model cancer gene expression, and whether or not such a VAE would capture biologically-relevant features. In the following report, we introduce a VAE trained on TCGA pan-cancer RNA-seq data, identify specific patterns in the VAE encoded features, and discuss potential merits of the approach. We name our method "Tybalt" after an instigative, cat-like character who sets a cascading chain of events in motion in Shakespeare's "Romeo and Juliet". From a systems biology perspective, Tybalt could one day aid in cancer stratification or predict specific activated expression patterns that would result from genetic changes or treatment effects.

关键词： Deep Learning Gene Expression variational autoencoder The Cancer Genome Atlas

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Regularizing variational autoencoders for Molecular Graph Generation 1

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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...

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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...

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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...

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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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