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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Baur, Michael Turan, Nurettin Fesl, Benedikt Utschick, Wolfgang Tech Univ Munich TUM Sch Computat Informat & Technol Munich Germany

ISBN: (纸本)9798350344868;9798350344851

In this work, we propose to utilize a variational autoencoder (VAE) for channel estimation (CE) in underdetermined (UD) systems. The basis of the method forms a recently proposed concept in which a VAE is trained on channel state information (CSI) data and used to parameterize an approximation to the mean squared error (MSE)-optimal estimator. The contributions in this work extend the existing framework from fully-determined (FD) to UD systems, which are of high practical relevance. Particularly noteworthy is the extension of the estimator variant, which does not require perfect CSI during its offline training phase. This is a significant advantage compared to most other deep learning (DL)-based CE methods, where perfect CSI during the training phase is a crucial prerequisite. Numerical simulations for hybrid and wideband systems demonstrate the excellent performance of the proposed methods compared to related estimators.

关键词： Channel estimation wideband system hybrid system generative model variational autoencoder

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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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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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Achieving deep clustering through the use of variational autoencoders and similarity-based loss

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第10期19卷 10344-10360页

作者： Ma, He Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150000 Peoples R China

Clustering is an important and challenging research topic in many fields. Although various clustering algorithms have been developed in the past, traditional shallow clustering algorithms cannot mine the underlying structural information of the data. Recent advances have shown that deep clustering can achieve excellent performance on clustering tasks. In this work, a novel variational autoencoder-based deep clustering algorithm is proposed. It treats the Gaussian mixture model as the prior latent space and uses an additional classifier to distinguish different clusters in the latent space accurately. A similarity-based loss function is proposed consisting specifically of the cross-entropy of the predicted transition probabilities of clusters and the Wasserstein distance of the predicted posterior distributions. The new loss encourages the model to learn meaningful cluster-oriented representations to facilitate clustering tasks. The experimental results show that our method consistently achieves competitive results on various data sets.

关键词： clustering deep learning data representation variational autoencoder network architecture

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Synthetic Electrochemical Biosensor Data with Conditional variational autoencoders for Enhanced Predictive Modeling

Synthetic Electrochemical Biosensor Data with Conditional Va...

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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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Data Augmentation and Recognition for Plunger Lift Based on variational autoencoders 6

Data Augmentation and Recognition for Plunger Lift Based on ...

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6th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2024

作者： Xu, Zhiyu Lin, Hai Jia, Yujiang Du, Jing Mao, Jianjun Zhu, Jianjun Chen, Jun Li, Fangming Li, Nan Petro China Qinghai Oilfield Dunhuang China China University of Petroleum Beijing China Research Institute of Petroleum Exploration & Development Beijing China

ISBN: (纸本)9798331516550

Plunger lift technology effectively addresses liquid loading issues in the later stages of natural gas well production. With the continuous development of artificial intelligence technology, the rapid identification of plunger data has become a challenge in field operations. Due to the lack of labeled field data and significant data imbalance, it is difficult to directly identify using discriminative models. This paper proposes a plunger lift generative model based on variational autoencoders (VAEs) for data augmentation of pressure data in plunger lift processes. The model maps original data through an encoder to obtain a high-dimensional latent variable probability distribution;the decoder maps the latent variable back to the real space to generate samples. The generated data supplements the original data, which is then input into a discriminative network for training. The results show that compared to the original discriminative model, the model's performance indicators have improved after data augmentation. © 2024 IEEE.

关键词： Liquid loading Plunger lift variational autoencoder

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Generative Data by β-variational autoencoders Help Build Stronger Classifiers: ECG Use Case 19

Generative Data by β-Variational Autoencoders Help Build St...

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19th International Symposium on Medical Information Processing and Analysis (SIPAIM)

作者： Nademi, Yousef Kalmady, Sunil V. Sun, Weijie Salimi, Amir Hindle, Abram Kaul, Padma Greiner, Russell Univ Alberta Dept Comp Sci Edmonton AB Canada Univ Alberta Dept Med Canadian VIGOUR Ctr Edmonton AB Canada Univ Alberta Dept Med Edmonton AB Canada Alberta Machine Intelligence Inst Edmonton AB Canada

ISBN: (纸本)9798350325232

We explore the challenge of learning models that use electrocardiogram (ECG) data to diagnose various cardiovascular diseases. Here, we explore whether classifiers trained on a dataset of real labeled ECGs, augmented with synthetic ECGs, can perform better than ones trained on unaugmented datasets. We first used a dataset of ECGs, each labelled with one or more of 15 diagnoses, from 244,077 patients to train an unsupervised beta-VAE model, that could generate time series of 12-lead ECG signals for each of the diagnoses. We then used this generative model to generate ECGs with the ST-segment Elevated (STE) abnormality, which we added to the public dataset of ECG abnormalities (n = 6877, over normal (Sinus Rhythm) and 8 different abnormalities) of China Physiological Signal Challenge 2018, and found a learner trained on this extended dataset performed better than one trained on only the original data on the targeted STE label but also enhanced its performance for the classification of 4 other labels.

关键词： Electrocardiogram Machine Learning variational autoencoder Data Generation

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Semi-supervised variational autoencoders for Regression: Application to Soft Sensors 21

Semi-supervised Variational Autoencoders for Regression: App...

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IEEE 21st International Conference on Industrial Informatics (INDIN)

作者： Zhuang, Yilin Zhou, Zhuobin Alakent, Burak Mercangoz, Mehmet Imperial Coll London Dept Chem Engn London England Imperial Coll London Dept Math London England Bogazici Univ Dept Chem Engn Istanbul Turkiye

ISBN: (纸本)9781665493130

We present the development of a semi-supervised regression method using variational autoencoders (VAE) for soft sensing of process quality variables. Recently, use of VAEs was proposed for regression applications based on variational inference. In this work, We extend this approach of supervised VAEs for regression to make it learn from both labelled and unlabelled data leading to a semi-supervised VAE for regression (SSVAER) formulation. The probabilistic regressor resulting from the variational approach makes it possible to estimate the variance of the predictions simultaneously, which provides a means for online uncertainty quantification for soft sensors. We provide an extensive comparative study of SSVAER with previously proposed semi-supervised learning methods on two soft sensing benchmark problems using fixed-size datasets, where we vary the percentage of labelled data available for training. In these experiments, SSVAER achieves the lowest test errors in 11 of the 20 studied cases, compared to other methods where the second best method gets 4 lowest test errors out of the 20.

关键词： Semi-supervised learning soft sensors variational autoencoder uncertainty analysis

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Multilevel Anomaly Detection Through variational autoencoders and Bayesian Models for Self-Aware Embodied Agents

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IEEE TRANSACTIONS ON MULTIMEDIA 2022年 24卷 1399-1414页

作者： Slavic, Giulia Baydoun, Mohamad Campo, Damian Marcenaro, Lucio Regazzoni, Carlo Univ Genoa DITEN Fac Engn I-16145 Genoa Italy Univ Genoa DITEN I-16145 Genoa Italy

Anomaly detection constitutes a fundamental step in developing self-aware autonomous agents capable of continuously learning from new situations, as it enables to distinguish novel experiences from already encountered ones. This paper combines Dynamic Bayesian Networks (DBNs) and Neural Networks (NNs) and proposes a method for detecting anomalies in video data at different abstraction levels. We use a variational autoencoder (VAE) to reduce the dimensionality of video frames, and Optical Flows between subsequent images, generating a latent space that captures both visual and dynamical information and that is comparable to low-dimensional sensory data (e.g., positioning, steering angle). An Adapted Markov Jump Particle Filter is employed to predict the following frames and detect anomalies in video data. Our method's evaluation is executed using different video data from a semi-autonomous vehicle performing different tasks in a closed environment. Tests on benchmark anomaly detection datasets have additionally been conducted.

关键词： Feature extraction Anomaly detection Predictive models Image reconstruction Self-aware Probabilistic logic Data models Anomaly detection kalman filtering particle filtering variational autoencoder

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Using Shapley Values and variational autoencoders to Explain Predictive Models with Dependent Mixed Features

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JOURNAL OF MACHINE LEARNING RESEARCH 2022年第1期23卷 1-51页

作者： Olsen, Lars Henry Berge Glad, Ingrid Kristine Jullum, Martin Aas, Kjersti Univ Oslo Dept Math Moltke Moes vei 35Niels Henrik Abels hus N-0851 Oslo Norway Norwegian Comp Ctr Gaustadalleen 23aKristen Nygaards hus N-0373 Oslo Norway

Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models. Shapley values have desirable theoret-ical properties and a sound mathematical foundation in the field of cooperative game theory. Precise Shapley value estimates for dependent data rely on accurate modeling of the depen-dencies between all feature combinations. In this paper, we use a variational autoencoder with arbitrary conditioning (VAEAC) to model all feature dependencies simultaneously. We demonstrate through comprehensive simulation studies that our VAEAC approach to Shapley value estimation outperforms the state-of-the-art methods for a wide range of settings for both continuous and mixed dependent features. For high-dimensional settings, our VAEAC approach with a non-uniform masking scheme significantly outperforms competing meth-ods. Finally, we apply our VAEAC approach to estimate Shapley value explanations for the Abalone data set from the UCI Machine Learning Repository.

关键词： Explainable artificial intelligence prediction explanation Shapley values variational autoencoder feature dependence

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