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MC-RVAE: Multi-channel recurrent variational autoencoder for multimodal Alzheimer's disease progression modelling

NEUROIMAGE 2023年 268卷 119892-119892页

作者： Marti-Juan, Gerard Lorenzi, Marco Piella, Gemma Univ Pompeu Fabra Dept Tecnol Informacio & Comunicac BCN MedTech Barcelona Spain Univ Cote Azur Epione Res Project Inria Sophia Antipolis Nice France

The progression of neurodegenerative diseases, such as Alzheimer's Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with N = 897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores.

关键词： Alzheimer's disease Longitudinal Multimodal variational autoencoder Recurrent neural network Disease progression modelling

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Interpretation for variational autoencoder Used to Generate Financial Synthetic Tabular Data

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ALGORITHMS 2023年第2期16卷 121-121页

作者： Wu, Jinhong Plataniotis, Konstantinos Liu, Lucy Amjadian, Ehsan Lawryshyn, Yuri Univ Toronto Dept Chem Engn & Appl Chem Toronto ON M5S 3E5 Canada Univ Toronto Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada Royal Bank Canada Toronto ON M5J 0B6 Canada Univ Waterloo David R Cheriton Sch Comp Sci Toronto ON N2L 3G1 Canada

Synthetic data, artificially generated by computer programs, has become more widely used in the financial domain to mitigate privacy concerns. variational autoencoder (VAE) is one of the most popular deep-learning models for generating synthetic data. However, VAE is often considered a "black box" due to its opaqueness. Although some studies have been conducted to provide explanatory insights into VAE, research focusing on explaining how the input data could influence VAE to create synthetic data, especially for tabular data, is still lacking. However, in the financial industry, most data are stored in a tabular format. This paper proposes a sensitivity-based method to assess the impact of inputted tabular data on how VAE synthesizes data. This sensitivity-based method can provide both global and local interpretations efficiently and intuitively. To test this method, a simulated dataset and three Kaggle banking tabular datasets were employed. The results confirmed the applicability of this proposed method.

关键词： interpretability variational autoencoder financial synthetic tabular data sensitivity-based method feature importance feature interaction

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Modeling Cycle-to-Cycle Variations of a Spark-Ignited Gas Engine Using Artificial Flow Fields Generated by a variational autoencoder

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ENERGIES 2022年第7期15卷 2325页

作者： Posch, Stefan Goessnitzer, Clemens Ofner, Andreas B. Pirker, Gerhard Wimmer, Andreas LEC GmbH Inffeldgasse 19 A-8010 Graz Austria Graz Univ Technol Know Ctr GmbH Inffeldgasse 13 A-8010 Graz Austria Graz Univ Technol Inst Thermodynam & Sustainable Prop Syst Inffeldgasse 19 A-8010 Graz Austria

A deeper understanding of the physical nature of cycle-to-cycle variations (CCV) in internal combustion engines (ICE) as well as reliable simulation strategies to predict these CCV are indispensable for the development of modern highly efficient combustion engines. Since the combustion process in ICE strongly depends on the turbulent flow field in the cylinder and, for spark-ignited engines, especially around the spark plug, the prediction of CCV using computational fluid dynamics (CFD) is limited to the modeling of turbulent flows. One possible way to determine CCV is by applying large eddy simulation (LES), whose potential in this field has already been shown despite its drawback of requiring considerable computational time and resources. This paper presents a novel strategy based on unsteady Reynolds-averaged Navier-Stokes (uRANS) CFD in combination with variational autoencoders (VAEs). A VAE is trained with flow field data from presimulated cycles at a specific crank angle. Then, the VAE can be used to generate artificial flow fields that serve to initialize new CFD simulations of the combustion process. With this novel approach, a high number of individual cycles can be simulated in a fraction of the time that LES needs for the same amount of cycles. Since the VAE is trained on data from presimulated cycles, the physical information of the cycles is transferred to the generated artificial cycles.

关键词： internal combustion engine combustion CFD RANS simulation cycle-to-cycle varations variational autoencoder

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variational autoencoder Based Fault Detection and Location Method for Power Distribution Network 8

Variational Autoencoder Based Fault Detection and Location M...

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8th International Conference on Condition Monitoring and Diagnosis (CMD)

作者： Wang, Xuhong Cui, Ping Du, Ying Yang, Yupu Shanghai Jiao Tong Univ Dept Automat Minist Educ Key Lab Syst Control & Informat Proc Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Elect Engn State Energy Smart Grid R&D Ctr Shanghai Peoples R China

ISBN: (纸本)9781728159317

With the rapid development of the world economy and the continuous improvement of people's living standards, users put forward higher requirements for the quality and reliability of the power system. As a representative of deep learning, variational autoencoders play an important role in processing high-dimensional big data. In this paper, a novel variational autoencoder based distribution network abnormal monitoring and positioning method is proposed. Through an in-depth study of the variational autoencoder, we utilize the real-time dynamic distribution network status information provided by the wide-area measurement system, combined with the abilities of feature extracting and data reconstruction. Finally, the IEEE-33 simulation model was built in Matlab software and the experimental results were used to verify the correctness of this method. The results show that this method can quickly and accurately achieve the fault detection and location for the distribution network with strong robustness.

关键词： Anomaly detetion Distribution systems Fault location variational autoencoder

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HiCoVA: Hierarchical Conditional variational autoencoder for Keyphrase Generation 21

HiCoVA: Hierarchical Conditional Variational Autoencoder for...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Santosh, T. Y. S. S. Reddy, Nikhil, V Anoop, V Sanyal, Debarshi Kumar Das, Partha Pratim Microsoft Hyderabad India IIT Kharagpur Kharagpur India Indian Assoc Cultivat Sci Kolkata India

ISBN: (纸本)9781450384469

The task of keyphrase generation, unlike extraction, aims to generate the phrases which succinctly capture the key information of the source text, that are even absent in the document (i.e., do not match any contiguous sub-sequence of source text). Despite the significant progress achieved by sequence-to-sequence (seq2seq) models in modelling such a high entropy task, they are limited by their deterministic modelling capability which limits the generation of a diverse set of keyphrases. To address the above limitation, in this paper, we propose to incorporate Conditional variational autoencoder (CoVA) into seq2seq models for its ability to represent a set of keyphrases as a probabilistic distribution, which improves the diversity of the generated keyphrases. We model the probabilistic distribution using a hierarchical latent structure where a global latent variable tries to model the diversity among the keyphrases and local latent variables control the generation of each keyphrase to make them coherent. Experimental results on four benchmark datasets of research papers demonstrate the effectiveness of our proposed approach in achieving a large improvement in diversity along with modest gains in quality with respect to previous models.

关键词： Keyphrase generation variational autoencoder diversity

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Learning to Model the Grasp Space of an Underactuated Robot Gripper Using variational autoencoder 19

Learning to Model the Grasp Space of an Underactuated Robot ...

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19th IFAC Symposium on System Identification (SYSID)

作者： Rolinat, Clement Grossard, Mathieu Aloui, Saifeddine Godin, Christelle Univ Paris Saclay LIST CEA F-91120 Palaiseau France Univ Grenoble Alpes Leti CEA F-38000 Grenoble France

Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents a data-driven oriented methodology to model the grasp space of a multi-fingered adaptive gripper for known objects. This method relies on a limited dataset of manually specified expert grasps, and uses variational autoencoder to learn grasp intrinsic features in a compact way from a computational point of view. The learnt model can then be used to generate new non-learnt gripper configurations to explore the grasp space. Copyright (C) 2021 The Authors.

关键词： multi-fingered gripper grasp space exploration variational autoencoder

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LSTMVAEF: Vivid Layout via LSTM-Based variational autoencoder Framework 16th

LSTMVAEF: Vivid Layout via LSTM-Based Variational Autoencode...

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16th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： He, Jie Wu, Xingjiao Hu, Wenxin Yang, Jing East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9783030863319

The lack of training data is still a challenge in the Document Layout Analysis task (DLA). Synthetic data is an effective way to tackle this challenge. In this paper, we propose an LSTM-based variational autoencoder framework (LSTMVAF) to synthesize layouts for DLA. Compared with the previous method, our method can generate more complicated layouts and only need training data from DLA without extra annotation. We use LSTM models as basic models to learn the potential representing of class and position information of elements within a page. It is worth mentioning that we design a weight adaptation strategy to help model train faster. The experiment shows our model can generate more vivid layouts that only need a few real document pages.

关键词： Document Layout Analysis variational autoencoder Document generation

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GF-VAE: A Flow-based variational autoencoder for Molecule Generation 21

GF-VAE: A Flow-based Variational Autoencoder for Molecule Ge...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Ma, Changsheng Zhang, Xiangliang King Abdullah Univ Sci & Technol Jeddah Saudi Arabia

ISBN: (纸本)9781450384469

Generating novel molecules with desired properties is a fundamental problem in modern drug discovery. This is a challenging problem because it requires the optimization of the given objectives while obeying the rules of chemical valence. An effective approach is to incorporate the molecular graph with deep generative models. However, recent generative models with high-performance are still computationally expensive. In this paper, we propose GF-VAE, a flow-based variational autoencoder (VAE) model for molecular graph generation. Specifically, the model equips VAE a lightweight flow model as its decoder, in which, the encoder aims to accelerate the training process of the decoder, while the decoder in turns to optimize the performance of the encoder. Thanks to the invertibility of flow model, the generation process is easily accomplished by reversing the decoder. Additionally, the final generated molecules are processed by validity correction. Therefore, our GF-VAE inherits the advantages of both VAE and flow-based methods. We validate our model on molecule generation and reconstruction, smoothness of learned latent space, property optimization and constrained property optimization. The results show that our model achieves state-of-the-arts performance on these tasks. Moreover, the time performance of GF-VAE on two classical datasets can achieve 31.3% and 62.9% improvements separately than the state-of-the-art model.

关键词： Graph Generative Model variational autoencoder Normalizing Flow Molecular Generation Molecular property optimization

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Radar Image Reconstruction from Raw ADC Data using Parametric variational autoencoder with Domain Adaptation 25

Radar Image Reconstruction from Raw ADC Data using Parametri...

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25th International Conference on Pattern Recognition (ICPR)

作者： Stephan, Michael Stadelmayer, Thomas Santra, Avik Fischer, Georg Weigel, Robert Lurz, Fabian Infineon Technol AG Neubiberg Germany Friedrich Alexander Univ Erlangen Nuremberg Erlangen Germany

ISBN: (纸本)9781728188089

This paper presents a parametric variational autoencoder-based human target detection and localization framework working directly with the raw analog-to-digital converter data from the frequency modulated continuous wave radar. We propose a parametrically constrained variational autoencoder, with residual and skip connections, capable of generating the clustered and localized target detections on the range-angle image. Furthermore, to circumvent the problem of training the proposed neural network on all possible scenarios using real radar data, we propose domain adaptation strategies whereby we first train the neural network using ray tracing based model data and then adapt the network to work on real sensor data. This strategy ensures better generalization and scalability of the proposed neural network even though it is trained with limited radar data. We demonstrate the superior detection and localization performance of our proposed solution compared to the conventional signal processing pipeline and earlier state-of-art deep U-Net architecture with range-doppler images as inputs.

关键词： Detection and Localization Parametric Deep Neural Network variational autoencoder Domain Adaptation

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variational autoencoder Anomaly-Detection of Avalanche Deposits in Satellite SAR Imagery 10

Variational Autoencoder Anomaly-Detection of Avalanche Depos...

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10th International Conference on Climate Informatics (CI)

作者： Sinha, Saumya Giffard-Roisin, Sophie Karbou, Fatima Deschatres, Michael Karas, Anna Eckert, Nicolas Coleou, Cecile Monteleoni, Claire Univ Colorado Boulder CO 80309 USA Univ Grenoble Alpes ISTerre Univ Savoie Mt Blanc CNRSIRDIFSTTAR Grenoble France Meteo France CNRM GAME Grenoble France Univ Grenoble Alpes Irstea Grenoble France

ISBN: (纸本)9781450388481

This work demonstrates that deep unsupervised learning holds much promise for rare event detection, even when labeled data is limited. Rare event detection is challenging for traditional supervised learning approaches due to high class-imbalance. We demonstrate the efficacy of deep unsupervised learning, in particular a variational autoencoder (VAE), when used for anomaly detection, in an application to avalanche detection in the French Alps, from satellite SAR imagery and a limited on-the-ground survey. Remarkably, our results demonstrate that supervision (i.e., access to labeled data) is only needed in the validation phase of our training pipeline, to tune a hyper-parameter: the threshold on reconstruction error of the VAE (trained in an unsupervised fashion) that will be used to designate an observation as an anomaly, i.e., an avalanche in this context. Our method outperforms previous benchmarks in avalanche detection, including supervised learning a convolutional neural network on an artificially balanced version of the same data. To the best of our knowledge, this is the first step in exploring the potential of applying deep unsupervised learning methods to detect avalanche deposits. Our proposed semi-supervised learning pipeline is also promising for real-world settings in which the data is very unbalanced and labels are limited.

关键词： unsupervised deep learning anomaly detection variational autoencoder avalanche detection satellite imaging

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