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International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

作者： Hamghalam, Mohammad Frangi, Alejandro F. Lei, Baiying Simpson, Amber L. Queens Univ Sch Comp Kingston ON Canada Islamic Azad Univ Fac Elect Qazvin Branch Qazvin Iran Queens Univ Dept Biomed & Mol Sci Kingston ON Canada Univ Leeds CISTIB Ctr Computat Imaging & Simulat Technol Bio Sch Comp Leeds LS2 9LU W Yorkshire England LICAMM Leeds Inst Cardiovasc & Metab Med Sch Med Leeds LS2 9LU W Yorkshire England Katholieke Univ Leuven Med Imaging Res Ctr MIRC Univ Hosp Gasthuisberg Herestr 49 B-3000 Leuven Belgium Shenzhen Univ Hlth Sci Ctr Sch Biomed EngnGuangdong Key Lab Biomed Measurem Natl Reg Key Technol Engn Lab Med Ultrasound Shenzhen Peoples R China

ISBN: (纸本)9783030872342;9783030872335

In large studies involving multi protocol Magnetic Resonance Imaging (MRI), it can occur to miss one or more sub-modalities for a given patient owing to poor quality (e.g. imaging artifacts), failed acquisitions, or hallway interrupted imaging examinations. In some cases, certain protocols are unavailable due to limited scan time or to retrospectively harmonise the imaging protocols of two independent studies. Missing image modalities pose a challenge to segmentation frameworks as complementary information contributed by the missing scans is then lost. In this paper, we propose a novel model, Multi-modal Gaussian Process Prior variational autoencoder (MGP-VAE), to impute one or more missing sub-modalities for a patient scan. MGP-VAE can leverage the Gaussian Process (GP) prior on the variational autoencoder (VAE) to utilize the subjects/patients and sub-modalities correlations. Instead of designing one network for each possible subset of present sub-modalities or using frameworks to mix feature maps, missing data can be generated from a single model based on all the available samples. We show the applicability of MGP-VAE on brain tumor segmentation where either, two, or three of four sub-modalities may be missing. Our experiments against competitive segmentation baselines with missing sub-modality on BraTS'19 dataset indicate the effectiveness of the MGP-VAE model for segmentation tasks.

关键词： Missing modality Gaussian process variational autoencoder Glioma segmentation MRI

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Mixtures of variational autoencoders 10

Mixtures of Variational Autoencoders

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10th International Conference on Image Processing Theory, Tools and Applications (IPTA)

作者： Ye, Fei Bors, Adrian G. Univ York Dept Comp Sci York YO10 5GH N Yorkshire England

ISBN: (纸本)9781728187501

In this paper, we develop a new deep mixture learning framework, aiming to learn underlying complex data structures. Each component in the mixture model is implemented using a variational autoencoder (VAE). VAE is a well known deep learning model which models a latent space data representation on a variational manifold. The mixing parameters are estimated from a Dirichlet distribution modelled by each encoder. In order to train this mixture model, named M-VAE, we derive a mixture evidence lower bound on the sample log-likelihood, which is optimized in order to jointly estimate all mixture components. We further propose to use the d-variables Hilbert-Schmidt Independence Criterion (dHSIC) as a regularization criterion in order to enforce the independence among the encoders' distributions. This criterion encourages the proposed mixture components to learn different data distributions and represent them in the latent space. During the experiments with the proposed M-VAE model we observe that it can be used for discovering disentangled data representations which can not be achieved with a single VAE.

关键词： Mixture models variational autoencoder Hilbert-Schmidt Independence Criterion

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Generating Synthetic Flight Tracks for Collision Risk Safety Analysis: variational autoencoders with a Single Seed Track 24

Generating Synthetic Flight Tracks for Collision Risk Safety...

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Integrated Communications, Navigation and Surveillance Conference (ICNS)

作者： Aref, Shahab Shortle, John Sherry, Lance George Mason Univ Ctr Air Transportat Syst Res Syst Engn & Operat Res Fairfax VA 22030 USA

ISBN: (纸本)9798350393101;9798350393095

Collision Risk Models (CRM), used by regulatory authorities to approve new procedures and/or technology, assess the probability of air-to-air collisions against a Target Level of Safety (e.g. 10E-9). A key component of the CRM is a variety of plausible flight tracks. Historic flight tracks can be blended with simulated flight tracks to generate a sample of tracks for analysis, however, even using super-computers, the sample size of flight tracks is not sufficient for a narrow confidence interval for the probability of a collision. This paper describes the results of the application of an AI/ML algorithm known as variational autoencoders (VAE) to increase the sample size of flight tracks for CRM. 2,356 flight tracks for the Ondre 1 arrival to Runway 26R at Atlanta airport were used to train a VAE. Next a single "seed" track, representing a nominal trajectory, was used to generate 2,356 synthetic flight tracks. By properly adjusting the VAE coefficients, the generated synthetic tracks matched historic tracks with regards to mean Along Track Distance (ATD), Total Cross Track Distance (TCTD), vertical dispersion at 9nm and 6nm from the Runway, and lateral dispersion at 9nm and 6nm from the Runway. However, in all the metrics the variability in the flight tracks was significantly smaller than the variability in the historic tracks. These promising results suggest that an approach using multiple "seed tracks" could achieve the desired variability. The implications of these results and future work are discussed.

关键词： variational autoencoder Synthetic flight track Collision risk Hartsfield-Jackson Atlanta International Airport (ATL) Along-track Distance Total Cross Track Distance Lateral dispersion Vertical dispersion

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SELF-ORGANIZED variational autoencoderS (SELF-VAE) FOR LEARNED IMAGE COMPRESSION

SELF-ORGANIZED VARIATIONAL AUTOENCODERS (SELF-VAE) FOR LEARN...

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IEEE International Conference on Image Processing (ICIP)

作者： Yilmaz, M. Akin Keles, Onur Guven, Hilal Tekalp, A. Murat Malik, Junaid Kiranyaz, Serkan Koc Univ Dept Elect & Elect Engn TR-34450 Istanbul Turkey Tampere Univ Tampere Finland Qatar Univ Doha Qatar

ISBN: (纸本)9781665441155

In end-to-end optimized learned image compression, it is standard practice to use a convolutional variational autoencoder with generalized divisive normalization (GDN) to transform images into a latent space. Recently, Operational Neural Networks (ONNs) that learn the best non-linearity from a set of alternatives, and their "self-organized" variants, Self-ONNs, that approximate any non-linearity via Taylor series have been proposed to address the limitations of convolutional layers and a fixed nonlinear activation. In this paper, we propose to replace the convolutional and GDN layers in the variational autoencoder with self-organized operational layers, and propose a novel self-organized variational autoencoder (Self-VAE) architecture that benefits from stronger non-linearity. The experimental results demonstrate that the proposed Self-VAE yields improvements in both rate-distortion performance and perceptual image quality.

关键词： end-to-end learned image compression variational autoencoder self-organized operational layer rate-distortion performance perceptual quality metrics

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ViVA: Semi-supervised Visualization via variational autoencoders 20

ViVA: Semi-supervised Visualization via Variational Autoenco...

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20th IEEE International Conference on Data Mining (ICDM)

作者： An, Sungtae Hong, Shenda Sun, Jimeng Georgia Inst Technol Coll Comp Atlanta GA 30332 USA Univ Illinois Dept Comp Sci Urbana IL USA

ISBN: (纸本)9781728183169

Visualizing latent embeddings is a popular approach to explain classification models, including deep neural networks. However, existing visualization methods such as t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation Projection (UMAP) are often used as a postprocessing step which is independent of the classification models. The resulting visualization can be misaligned with the classification models. In this paper, we propose ViVA, a novel method for semi-supervised Visualization via variational autoencoders. ViVA learns from both unlabeled and labeled data by jointly optimizing both visualization loss and classification loss. As a parameterized model using neural networks, ViVA can easily project new data to the same embedding space. Experiments show that ViVA can achieve better visualization quality as well as classification accuracy on multiple challenging datasets compared to several visualization baselines, including t-SNE and UMAP.

关键词： semi-supervised learning variational autoencoder visualization

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An efficient stock market prediction model using hybrid feature reduction method based on variational autoencoders and recursive feature elimination

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Financial Innovation 2021年第1期7卷 585-608页

作者： Hakan Gunduz Software Engineering Department Bandirma Onyedi Eylul University10200 BalikesirTurkey

In this study,the hourly directions of eight banking stocks in Borsa Istanbul were predicted using linear-based,deep-learning(LSTM)and ensemble learning(Light-GBM)*** models were trained with four different feature sets and their performances were evaluated in terms of accuracy and F-measure *** the first experiments directly used the own stock features as the model inputs,the second experiments utilized reduced stock features through variational autoencoders(VAE).In the last experiments,in order to grasp the effects of the other banking stocks on individual stock performance,the features belonging to other stocks were also given as inputs to our *** combining other stock features was done for both own(named as allstock_own)and VAE-reduced(named as allstock_VAE)stock features,the expanded dimensions of the feature sets were reduced by Recursive Feature *** the highest success rate increased up to 0.685 with allstock_own and LSTM with attention model,the combination of allstock_VAE and LSTM with the attention model obtained an accuracy rate of *** the classification results achieved with both feature types was close,allstock_VAE achieved these results using nearly 16.67%less features compared to allstock_*** all experimental results were examined,it was found out that the models trained with allstock_own and allstock_VAE achieved higher accuracy rates than those using individual stock *** was also concluded that the results obtained with the VAE-reduced stock features were similar to those obtained by own stock features.

关键词： Stock market prediction variational autoencoder Recursive feature elimination Long-short term memory Borsa Istanbul LightGBM

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Pair-variational autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques

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JACS AU 2023年第9期3卷 2510-2521页

作者： Lu, Shizhao Jayaraman, Arthi Univ Delaware Dept Chem & Biomol Engn Newark DE 19716 USA Univ Delaware Dept Mat Sci & Engn Newark DE 19716 USA

In materials research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of a synthesized material. Depending on the availability and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab may have access to high-throughput capability in one technique but face limitations (sample preparation, resolution, access time) with other technique(s). Furthermore, one type of structural characterization data may be easier to interpret than another (e.g., microscopy images are easier to interpret than small-angle scattering profiles). Thus, it is useful to have machine learning models that can be trained on paired structural characterization data from multiple techniques (easy and difficult to interpret, fast and slow in data collection or sample preparation) so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, Pair-variational autoencoders (PairVAE), that works with data from small-angle X-ray scattering (SAXS) that present information about bulk morphology and images from scanning electron microscopy (SEM) that present two-dimensional local structural information on the sample. Using paired SAXS and SEM data of newly observed block copolymer assembled morphologies [open access data from ], we train our PairVAE. After successful training, we demonstrate that the PairVAE can generate SEM images of the block copolymer morphology when it takes as input that sample's corresponding SAXS 2D pattern and vice versa. This method can be extended to other soft material morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as an engine for generating ensembles of similar microscopy images to create a database for other downstream calculations of structure-property relationships.

关键词： machine learning material characterization scattering microscopy variational autoencoder

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Training variational autoencoders with Discrete Latent Variables Using Importance Sampling 27

Training Variational Autoencoders with Discrete Latent Varia...

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27th European Signal Processing Conference (EUSIPCO)

作者： Bartler, Alexander Wiewel, Felix Mauch, Lukas Yang, Bin Univ Stuttgart Inst Signal Proc & Syst Theory Stuttgart Germany

ISBN: (纸本)9789082797039

The variational autoencoder (VAE) is a popular generative latent variable model that is often used for representation learning. Standard VAEs assume continuous-valued latent variables and are trained by maximization of the evidence lower bound (ELBO). Conventional methods obtain a differentiable estimate of the ELBO with reparametrized sampling and optimize it with Stochastic Gradient Descend (SGD). However, this is not possible if we want to train VAEs with discrete-valued latent variables, since reparametrized sampling is not possible. In this paper, we propose an easy method to train VAEs with binary or categorically valued latent representations. Therefore, we use a differentiable estimator for the ELBO which is based on importance sampling. In experiments, we verify the approach and train two different VAEs architectures with Bernoulli and categorically distributed latent representations on two different benchmark datasets.

关键词： variational autoencoder discrete latent variables importance sampling

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Dual Sequential variational autoencoders for Fraud Detection 1

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18th International Symposium on Intelligent Data Analysis (IDA)

作者： Alazizi, Ayman Habrard, Amaury Jacquenet, Francois He-Guelton, Liyun Oble, Frederic Univ Lyon Lab Hubert Curien Univ St Etienne UMR CNRS 5516 F-42000 St Etienne France Worldline F-95870 Bezons France

ISBN: (数字)9783030445843

ISBN: (纸本)9783030445843;9783030445836

Fraud detection is an important research area where machine learning has a significant role to play. An important task in that context, on which the quality of the results obtained depends, is feature engineering. Unfortunately, this is very time and human consuming. Thus, in this article, we present the DuSVAE model that consists of a generative model that takes into account the sequential nature of the data. It combines two variational autoencoders that can generate a condensed representation of the input sequential data that can then be processed by a classifier to label each new sequence as fraudulent or genuine. The experiments we carried out on a large real-word dataset, from the Worldline company, demonstrate the ability of our system to better detect frauds in credit card transactions without any feature engineering effort.

关键词： Anomaly detection Fraud detection Sequential data variational autoencoder

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Deep variational autoencoders for breast cancer tissue modeling and synthesis in SFDI 7

Deep variational autoencoders for breast cancer tissue model...

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Conference on Diffuse Optical Spectroscopy and Imaging VII held at European Conferences on Biomedical Optics

作者： Pardo, Arturo Lopez-Higuera, Jose M. Pogue, Brian W. Conde, Olga M. Univ Cantabria Photon Engn Grp GIF TEISA Dept Edificio IDi TelecomuniacAvda Castros S-N E-39005 Santander Cantabria Spain Inst Invest Sanitaria Valdecilla IDIVAL Santander 39011 Cantabria Spain Biomed Res Networking Ctr Bioengn Nanomat & Nanos Ave Monforte de Lemos3-5 Pabellon 11Planta 0 Madrid 28029 Spain Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA

ISBN: (数字)9781510628427

ISBN: (纸本)9781510628410;9781510628427

Extracting pathology information embedded within surface optical properties in Spatial Frequency Domain Imaging (SFDI) datasets is still a rather cumbersome nonlinear translation problem, mainly constrained by intrasample and interpatient variability, as well as dataset size. The beta-variational autoencoder (beta-VAE) is a rather novel dimensionality reduction technique where a tractable set of latent low-dimensional embeddings can be obtained from a given dataset. These embeddings can then be sampled to synthesize new data, providing further insight into pathology variability as well as differentiability in terms of optical properties. Its applications for data classification and breast margin delineation are also discussed.

关键词： Deep learning modulated imaging optical properties spatial frequency domain imaging breast cancer variational autoencoder turbid media

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