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检索条件"主题词=variational autoencoder"

共 1537 条记录，以下是621-630 订阅

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Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics 24

Parameter tuning is a key part of dimensionality reduction v...

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24th Pacific Symposium on Biocomputing (PSB)

作者： Hu, Qiwen Greene, Casey S. Univ Penn Dept Syst Pharmacol & Translat Therapeut Philadelphia PA 19104 USA

ISBN: (纸本)9789813279827;9789813279810

Single-cell RNA sequencing (scRNA-seq) is a powerful tool to profile the transcriptomes of a large number of individual cells at a high resolution. These data usually contain measurements of gene expression for many genes in thousands or tens of thousands of cells, though some datasets now reach the million-cell mark. Projecting high-dimensional scRNA-seq data into a low dimensional space aids downstream analysis and data visualization. Many recent preprints accomplish this using variational autoencoders (VAE), generative models that learn underlying structure of data by compress it into a constrained, low dimensional space. The low dimensional spaces generated by VAEs have revealed complex patterns and novel biological signals from large-scale gene expression data and drug response predictions. Here, we evaluate a simple VAE approach for gene expression data, Tybalt, by training and measuring its performance on sets of simulated scRNA-seq data. We find a number of counter-intuitive performance features: i.e., deeper neural networks can struggle when datasets contain more observations under some parameter configurations. We show that these methods are highly sensitive to parameter tuning: when tuned, the performance of the Tybalt model, which was not optimized for scRNA-seq data, outperforms other popular dimension reduction approaches - PCA, ZIFA, UMAP and t-SNE. On the other hand, without tuning performance can also be remarkably poor on the same data. Our results should discourage authors and reviewers from relying on self-reported performance comparisons to evaluate the relative value of contributions in this area at this time. Instead, we recommend that attempts to compare or benchmark autoencoder methods for scRNA-seq data be performed by disinterested third parties or by methods developers only on unseen benchmark data that are provided to all participants simultaneously because the potential for performance differences due to unequal parameter tuning is so h

关键词： Single Cell variational autoencoder Dimensionality Reduction Latent Spaces

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Fault Detection Based on variational autoencoders for Complex Nonlinear Processes 12

Fault Detection Based on Variational Autoencoders for Comple...

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12th Asian Control Conference (ASCC)

作者： Wang, Kai Chen, Junghui Song, Zhihuan Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China Chung Yuan Christian Univ Dept Chem Engn Taoyuan 32023 Taiwan

ISBN: (纸本)9784888983006

Deep learning models have been proved to outperform shallow methods for industrial process fault detection because of their high capacity for complex nonlinearity. However, typical deep models applied to monitoring processes are conducted in a deterministic manner. They are unable to provide a confidence level for each decision. Also, most deep learning methods often need to integrate prior conditions, such as orthogonal latent variables, constraints, and some given distributions. The consequences of these issues cause lots of trials and errors as conventional deep models are built based on experiences. In this paper, a variational auto-encoder is used to set up a framework to tackle these problems. The learned latent variables, which would be orthogonal to each other, are constrained under the specified and optimized objective. Simultaneously, considering uncertainty in data, probability density estimates of latent variables and residuals instead of point estimates are given to design distribution based monitoring indices. A numerical example validates the effectiveness of the proposed method.

关键词： Fault detection variational Bayes variational autoencoder Nonlinear systems

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Period Singer: Integrating Periodic and Aperiodic variational autoencoders for Natural-Sounding End-to-End Singing Voice Synthesis 25

Period Singer: Integrating Periodic and Aperiodic Variationa...

引用

25th Interspeech Conference

作者： Kim, Taewoo Cho, Choonsang Lee, Young Han Korea Elect Technol Inst Seoul South Korea

In this paper, we present Period Singer, a novel end-to-end singing voice synthesis (SVS) model that utilizes variational inference for periodic and aperiodic components, aimed at producing natural-sounding waveforms. Recent end-to-end SVS models have demonstrated the capability of synthesizing high-fidelity singing voices. However, owing to deterministic pitch conditioning, they do not fully address the one-to-many problem. To address this problem, we present the Period Singer architecture, which integrates variational autoencoders for the periodic and aperiodic components. Additionally, our methodology eliminates the dependency on an external aligner by estimating the phoneme alignment through a monotonic alignment search within note boundaries. Our empirical evaluations show that Period Singer outperforms existing end-to-end SVS models on Mandarin and Korean datasets. The efficacy of the proposed method was further corroborated by ablation studies.

关键词： Singing voice synthesis end-to-end model variational autoencoder pitch modeling phoneme alignment

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Modeling and Transforming Speech using variational autoencoders 17

Modeling and Transforming Speech using Variational Autoencod...

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17th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2016)

作者： Blaauw, Merlijn Bonada, Jordi Univ Pompeu Fabra Mus Technol Grp Barcelona Spain

ISBN: (纸本)9781510833135

Latent generative models can learn higher-level underlying factors from complex data in an unsupervised manner. Such models can be used in a wide range of speech processing applications, including synthesis, transformation and classification. While there have been many advances in this field in recent years, the application of the resulting models to speech processing tasks is generally not explicitly considered. In this paper we apply the variational autoencoder (VAE) to the task of modeling frame-wise spectral envelopes. The VAE model has many attractive properties such as continuous latent variables, prior probability over these latent variables, a tractable lower bound on the marginal log likelihood, both generative and recognition models, and end-to-end training of deep models. We consider different aspects of training such models for speech data and compare them to more conventional models such as the Restricted Boltzmann Machine (RBM). While evaluating generative models is difficult, we try to obtain a balanced picture by considering both performance in terms of reconstruction error and when applying the model to a series of modeling and transformation tasks to get an idea of the quality of the learned features.

关键词： generative models variational autoencoder acoustic modeling deep learning

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Optimally designed variational autoencoders for Efficient Wind Characteristics Modelling

Optimally designed Variational Autoencoders for Efficient Wi...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Miriyala, Srinivas Soumitri Chowdhury, Subhankar Pujari, NagaSree Keerthi Mitra, Kishalay Indian Inst Technol Dept Chem Engn Hyderabad India

ISBN: (纸本)9781728125473

Wind energy is increasingly applied as a large scale clean energy generating alternative to fossil fuels. However, limited amount of real wind data results in inaccurate construction of Wind Frequency Maps (WFMs), which model the stochastic nature of wind. The inaccuracies in WFMs may lead to over or under estimation of wind power eventually causing significant losses to wind-farmers. Hence, to resolve this crisis, deep generative models such as convolutional variational autoencoders (VAEs) are implemented in this work to enable accurate construction of WFMs from limited amount of real wind characteristics data. However, the heuristics based estimation of hyper-parameters in VAEs decrease their efficiency. Thus, in this work, a novel multi-objective evolutionary neural architecture search (NAS) strategy is devised for simultaneously estimating the optimal number of convolutional and feedforward layers, number of filters/nodes in each layer, filter size, pooling option and nonlinear activation choice in VAEs. The proposed framework is designed to balance the conflicting objectives of generalizability and parsimony in VAEs, thereby reducing the chances of their over-fitting. The optimally designed VAE (with 92% accuracy) is used to generate new wind frequency scenarios for accurate construction of WFM. Additionally, the effect of number of new scenarios required for accurate WFM construction is also studied while performing the comparison with an ideal case. It was found that WFM constructed with original limited data resulted in 9% deficit in energy calculation from a single wind turbine, justifying the need for generative models such as VAEs for accurate wind characteristics modelling.

关键词： Deep Generative modelling Neural Architecture Search multi-objective optimization evolutionary algorithm variational autoencoder Wind energy conversion systems Wind frequency maps

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New Methods for Explainable variational autoencoders

New Methods for Explainable Variational Autoencoders

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Conference on Artificial Intelligence for Security and Defence Applications

作者： White, Riley Baracat-Donovan, Brian Helmsen, John McCullough, Thomas Noblis Inc 2022 Edmund Halley Dr Reston VA 20191 USA

ISBN: (纸本)9781510667136;9781510667143

A new deep learning algorithm for performing anomaly detection and multi-class classification with explainability using counterfactuals is described. The system is a variational autoencoder (VAE) with a modified loss function and new methods for counterfactual identification. An additional hinge-loss term is added to VAE training. This enables convenient synthetic data generation and allows straightforward construction of multi-class counterfactuals. Counterfactuals are synthetic data generated to explain system decisions by answering the question: "If this data was not anomalous or was in another class, what modifications would need to be made?" To determine counterfactuals, a path is determined through the embedding space via adversarial attack-like techniques to minimize reconstruction error, with the restriction of minimally altering the number of columns changed. Large changes are allowed, unlike adversarial attack approaches, so changes are isolated and easily visible. Anomaly detection is performed by modifying a result to lower its anomalousness. Classification changes are performed by modifying the data to another class. Multi-class classification is performed on the embedding space of the VAE via an attached linear support vector machine (SVM). By adding the hinge-loss term to the VAE embedding training as well as the SVM, the embedding is modified to prefer class separation without being informed of the specific class labels. This causes the classes in the embedding space to be separated by hyperplanes, making counterfactual generation convenient and SVM classification accurate. Accuracy is shown to be comparable to other deep learners. Approaches to accommodating the image and time-series data are discussed.

关键词： Deep Learning Anomaly Detection Explainability variational autoencoder

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Generalizing Across Domains in Diabetic Retinopathy via variational autoencoders 26th

Generalizing Across Domains in Diabetic Retinopathy via Vari...

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26th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) / 8th ISIC Workshop / 1st Care-AI Workshop / 1st MedAGI Workshop / 4th DeCaF Workshop

作者： Chokuwa, Sharon Khan, Muhammad H. Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031474002;9783031474019

Domain generalization for Diabetic Retinopathy (DR) classification allows a model to adeptly classify retinal images from previously unseen domains with various imaging conditions and patient demographics, thereby enhancing its applicability in a wide range of clinical environments. In this study, we explore the inherent capacity of variational autoencoders to disentangle the latent space of fundus images, with an aim to obtain a more robust and adaptable domain-invariant representation that effectively tackles the domain shift encountered in DR datasets. Despite the simplicity of our approach, we explore the efficacy of this classical method and demonstrate its ability to outperform contemporary state-of-the-art approaches for this task using publicly available datasets. Our findings challenge the prevailing assumption that highly sophisticated methods for DR classification are inherently superior for domain generalization. This highlights the importance of considering simple methods and adapting them to the challenging task of generalizing medical images, rather than solely relying on advanced techniques.

关键词： Domain Generalization Diabetic Retinopathy variational autoencoder

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Bidirectional variational autoencoders

Bidirectional Variational Autoencoders

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International Joint Conference on Neural Networks (IJCNN)

作者： Kosko, Bart Adigun, Olaoluwa Univ Southern Calif Dept Elect & Comp Engn Los Angeles CA 90007 USA

ISBN: (纸本)9798350359329;9798350359312

We present the new bidirectional variational autoencoder (BVAE) network architecture. The BVAE uses a single neural network both to encode and decode instead of an encoder-decoder network pair. The network encodes in the forward direction and decodes in the backward direction through the same synaptic web. Simulations compared BVAEs and ordinary VAEs on the four image tasks of image reconstruction, classification, interpolation, and generation. The image datasets included MNIST handwritten digits, Fashion-MNIST, CIFAR-10, and CelebA-64 face images. The bidirectional structure of BVAEs cut the parameter count by almost 50% and still slightly outperformed the unidirectional VAEs.

关键词： variational autoencoder bidirectional backpropagation directional likelihoods ELBO evidence lower bound

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Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and variational autoencoders 10th

Assessing the Impact of Blood Pressure on Cardiac Function U...

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10th International Workshop on Statistical Atlases and Computational Modelling of the Heart (STACOM)

作者： Puyol-Anton, Esther Ruijsink, Bram Clough, James R. Oksuz, Ilkay Rueckert, Daniel Razavi, Reza King, Andrew P. Kings Coll London Sch Biomed Engn & Imaging Sci London England St Thomas Hosp NHS Fdn Trust London England Imperial Coll London Biomed Image Anal Grp London England

ISBN: (纸本)9783030390747;9783030390730

Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health. The aim of this study is to analyze the impact of systolic blood pressure (SBP) on cardiac function while preserving the interpretability of the model using known clinical biomarkers in a large cohort of the UK Biobank population. We propose a novel framework that combines deep learning based estimation of interpretable clinical biomarkers from cardiac cine MR data with a variational autoencoder (VAE). The VAE architecture integrates a regression loss in the latent space, which enables the progression of cardiac health with SBP to be learnt. Results on 3,600 subjects from the UK Biobank show that the proposed model allows us to gain important insight into the deterioration of cardiac function with increasing SBP, identify key interpretable factors involved in this process, and lastly exploit the model to understand patterns of positive and adverse adaptation of cardiac function.

关键词： Cardiac function variational autoencoder Cardiac risk factors

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Enhancing Long Term Fairness in Recommendations with variational autoencoders 11

Enhancing Long Term Fairness in Recommendations with Variati...

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11th International Conference on Management of Digital EcoSystems (MEDES)

作者： Borges, Rodrigo Stefanidis, Kostas Univ Sao Paulo Sao Paulo Brazil Tampere Univ Tampere Finland

ISBN: (纸本)9781450362382

Recommender systems have become indispensable for several Web sites, helping users deal with big amounts of data. They are capable of analyzing user/item interactions taking place on-line, and provide each user with a list of suggestions sorted by relevance. Items with the same or very close relevance, however, may occupy different positions in the ranking and may be exposed to completely different levels of attention. This promotes unfair treatment and can only be addressed by a long term strategy. variational autoencoders (VAEs) were recently proposed as the state-of-the-art for collaborative filtering recommendations, but as every other approach, they generate homogeneous prediction scores among the highest positions. In this paper, we propose incorporating randomness in the regular operation of VAEs in order to increase the fairness (mitigate the position bias) in multiple rounds of recommendation. We argue that adding a noise component when sampling values from VAE's latent representation provides long term fairness, despite of a tolerable decrease in ranking quality (NDCG). We calculate the trade-off between unfairness and NDCG when introducing 4 different noise distributions. The solution has proved to be a very practical one and the results point for a clear positive effect of turning recommendation far more fair, despite some small NDCG loss in Movie Lens, Netflix and MSD datasets. In our best scenario, the unfairness was reduced by 76% despite a decrease of 5% in the quality of ranking.

关键词： Fairness in Ranking variational autoencoder Collaborative Filtering Recommendation Systems Position Bias

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