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15th International Conference on the Foundations of Digital Games

作者： Snodgrass, Sam Sarkar, Anurag Modlai Copenhagen Denmark Northeastern Univ Boston MA USA

ISBN: (纸本)9781450388078

Procedural content generation via machine learning (PCGML) has demonstrated its usefulness as a content and game creation approach, and has been shown to be able to support human creativity. An important facet of creativity is combinational creativity or the recombination, adaptation, and reuse of ideas and concepts between and across domains. In this paper, we present a PCGML approach for level generation that is able to recombine, adapt, and reuse structural patterns from several domains to approximate unseen domains. We extend prior work involving example-driven Binary Space Partitioning for recombining and reusing patterns in multiple domains, and incorporate variational autoencoders (VAEs) for generating unseen structures. We evaluate our approach by blending across 7 domains and subsets of those domains. We show that our approach is able to blend domains together while retaining structural components. Additionally, by using different groups of training domains our approach is able to generate both 1) levels that reproduce and capture features of a target domain, and 2) levels that have vastly different properties from the input domain.

关键词： procedural content generation level blending level generation binary space partitioning variational autoencoder PCGML

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Generation of Communication Interference Waveforms Based on variational autoencoders 23

Generation of Communication Interference Waveforms Based on ...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Chen, Shunshun Zhou, Zhichao Zhang, Zhipeng Wu, Xiangyu Hou, Changbo College of Information and Communication Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9798350325959

The generation of interference waveforms is one of the core technologies in communication countermeasures. Existing communication interference methods usually rely on signal information obtained from communication reconnaissance, but these methods are not ideal when facing unknown target signal characteristics or dynamically changing parameters. Therefore, this paper proposes a communication interference waveform generation method based on variational autoencoders (VAE). This method uses VAE to directly extract the latent features of the target signal and generate interference signals with similar characteristics. Through comparative experiments on interference effects, the results show that the interference effect of the interference waveform generated by VAE can approach the optimal interference effect, effectively meeting the requirements of communication interference. © 2023 IEEE.

关键词： Deep learning Interference waveform generation variational autoencoder

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High-dimensional numerical optimization of fiber reinforced polymers with variational autoencoders and Bayesian optimization

High-dimensional numerical optimization of fiber reinforced ...

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作者： Storm, Joep Delft University of Technology

学位级别：硕士

Designing engineering structures relies on accurate numerical simulations to predict the behaviour of a structure before its realization. In the design process, many variables influence the final structure. There is an incentive for optimizing the design based on the desire for cheaper structures and less material usage. Although a wide variety of optimization techniques exist, in practice many structures are not fully optimized and could fulfill their purpose with less material usage. This partly results from optimization techniques generally requiring many simulations for problems with a high number of variables. In creating increasingly complex models, simulating its performance can take up to days or weeks to compute, incentivizing the development of optimization methods that use as little simulations as possible. The goal of this thesis is to provide a method of optimization which significantly reduces the required number of simulations to be performed, aiming to overcome the issue of required computational effort. The optimization problem used in this thesis is the geometric optimization of a fiber reinforced polymer (FRP) microstructure. Each evaluation requires a Finite Element analysis, and many parameters are required to describe the geometry. The goal is to find the maximum stress at perfect plasticity during uniaxial tension, interpreted as a measure of strength. While generally only fibers with a circular cross-section are used, here a morphing parameter is introduced that changes this shape between being circular and square. Furthermore, fibers are allowed to overlap, effectively creating a single fiber with a complex shape. This geometry optimization provides a challenging problem for which different optimization techniques can be compared. The approach taken in attempting to minimize the number of function evaluations is a combination of several machine learning methods. Based on a limited number of initial samples, Bayesian optimization (BO) is appl

关键词： Bayesian Optimization variational autoencoder machine learning Regression high-dimensional Fiber Reinforced Polymers - FRP Artificial intelligence optimization Genetic Algorithm - GA Bayesian neural network Neural Network

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Augmenting time series data: An interpretable approach with metric learning and variational autoencoders

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2024年 161卷

作者： Zhang, Chunfeng Qin, Hao Zhang, Yongjun Jiang, Chongying Zhang, Di Deng, Wenyang South China Univ Technol Sch Elect Power Guangzhou 510665 Peoples R China

In the field of time series classification, deep learning techniques have shown remarkable performance;however, their effectiveness is often compromised when confronted with challenges of insufficient data and class imbalance. To address this challenge, we propose an interpretable time series data augmentation algorithm integrating variational autoencoders (VAE) and metric learning. The core contribution of this algorithm is manifested in three aspects: First, it eliminates the heteroscedasticity and non-stationarity of the data, ensuring that the data satisfies the hypothesis of normal distribution in the potential space of the encoder, and effectively avoids the approximation error of the real data distribution;Secondly, the algorithm constructs a discriminant VAE potential space, suitable for data augmentation, with metric learning, ensuring that the hidden variable distribution accurately reflects the characteristics of the original data. Finally, this paper explores the multi-seasonal decomposition algorithm of time series to seamlessly integrate the structural features of the original time series in the generated data, thereby enhancing the interpretability of data generation. Through experimental verification on four multivariate time series data sets, including the electrical energy data set, the results demonstrate that the proposed algorithm outperforms existing methods in fidelity and prediction performance, exhibiting high stability and generalization ability, particularly in cases of limited data volume. The introduction of this algorithm not only contributes to enhancing the overall performance of time series classification models but also substantially reduces the cost of data collection and labeling, thereby demonstrating its significant value in practical applications.

关键词： Time series data Data augmentation variational autoencoder Metric learning Time series decomposition

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An efficient dimensionality reduction method using filter-based feature selection and variational autoencoders on Parkinson's disease classification

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期66卷 102452-102452页

作者： Gunduz, Hakan Bandirma Onyedi Eylul Univ Engn & Nat Sci Fac Software Engn Dept TR-10200 Bandirma Balikesir Turkey

Parkinson's disease (Pd) is a progressive disease caused by the loss of brain cells and brings about speech and pronunciation defects during the early stages. This study revealed a Pd classification system based on vocal features extracted from the voice recordings of the individuals and proposed a hybrid dimensionality reduction methods to extract robust features. Proposed method took advantage of the prominent aspects of variational autoencoders (VAE) and filter-based feature selection models. Relief and Fisher Score were selected as filter-based methods for their effective performance in handling noisy data while VAE was used as a feature extractor due to the capability of preserving the regular latent space properties during the feature generation. In order to assess the effectiveness of the devised method, multi-kernel Support Vector Machines (SVM) classifier were trained with obtained deep feature representations. The combination of deep Relief features and SVM with multiple kernels distinguished Pd individuals from healthy subjects with an accuracy of 0.916 with 0.772 Matthews Correlation Coefficient (MCC) rates using only 30 features. Compared to results obtained without dimensionality reduction, proposed model provided approximately 9% and 22% improvements on accuracy and MCC rates, respectively. All experimental results showed that models trained with the deep features had higher accuracy and MCC rates with those trained with Fisher Score and Relief selected features. In addition, all models trained with reduced features had higher classification performance than the model without selection. It was also concluded that using multiple kernels in the SVM boosted the classification performance.

关键词： Parkinson's disease prediction Dimensionality reduction variational autoencoder Fisher score Relief Multi-Kernel SVM

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Discovering influential factors in variational autoencoders

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PATTERN RECOGNITION 2020年 100卷 107166-107166页

作者： Liu, Shiqi Liu, Jingxin Zhao, Qian Cao, Xiangyong Li, Huibin Meng, Deyu Meng, Hongying Liu, Sheng Xi An Jiao Tong Univ Xian Shaanxi Peoples R China Brunel Univ London London England SUNY Buffalo Buffalo NY 14214 USA

In the field of machine learning, it is still a critical issue to identify and supervise the learned representation without manually intervening or intuition assistance to extract useful knowledge or serve for the downstream tasks. In this work, we focus on supervising the influential factors extracted by the variational autoencoder (VAE). The VAE is proposed to learn independent low dimension representation while facing the problem that sometimes pre-set factors are ignored. We argue that the mutual information of the input and each learned factor of the representation plays a necessary indicator of discovering the influential factors. We find the VAE objective inclines to induce mutual information sparsity in factor dimension over the data intrinsic dimension and therefore result in some non-influential factors whose function on data reconstruction could be ignored. We show mutual information also influences the lower bound of VAE's reconstruction error and downstream classification task. To make such indicator applicable, we design an algorithm for calculating the mutual information for VAE and prove its consistency. Experimental results on MNIST, CelebA and DEAP datasets show that mutual information can help determine influential factors, of which some are interpretable and can be used to further generation and classification tasks, and help discover the variant that connects with emotion on DEAP dataset. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： variational autoencoder Mutual information Generative model

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PriorVAE: encoding spatial priors with variational autoencoders for small-area estimation

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JOURNAL OF THE ROYAL SOCIETY INTERFACE 2022年第191期19卷 20220094-20220094页

作者： Semenova, Elizaveta Xu, Yidan Howes, Adam Rashid, Theo Bhatt, Samir Mishra, Swapnil Flaxman, Seth Univ Oxford Oxford Oxon England Univ Michigan Ann Arbor MI USA Imperial Coll London London England Univ Copenhagen Copenhagen Denmark

Gaussian processes (GPs), implemented through multivariate Gaussian distributions for a finite collection of data, are the most popular approach in small-area spatial statistical modelling. In this context, they are used to encode correlation structures over space and can generalize well in interpolation tasks. Despite their flexibility, off-the-shelf GPs present serious computational challenges which limit their scalability and practical usefulness in applied settings. Here, we propose a novel, deep generative modelling approach to tackle this challenge, termed PriorVAE: for a particular spatial setting, we approximate a class of GP priors through prior sampling and subsequent fitting of a variational autoencoder (VAE). Given a trained VAE, the resultant decoder allows spatial inference to become incredibly efficient due to the low dimensional, independently distributed latent Gaussian space representation of the VAE. Once trained, inference using the VAE decoder replaces the GP within a Bayesian sampling framework. This approach provides tractable and easy-to-implement means of approximately encoding spatial priors and facilitates efficient statistical inference. We demonstrate the utility of our VAE two-stage approach on Bayesian, small-area estimation tasks.

关键词： spatial modelling Gaussian process prior small-area estimation Bayesian inference variational autoencoder

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Learning and Predicting Photonic Responses of Plasmonic Nanoparticle Assemblies via Dual variational autoencoders

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SMALL 2023年第25期19卷 2205893-2205893页

作者： Yaman, Muammer Y. Kalinin, Sergei V. Guye, Kathryn N. Ginger, David S. Ziatdinov, Maxim Univ Washington Dept Chem Seattle WA 98195 USA Univ Tennessee Dept Mat Sci & Engn Knoxville TN 37996 USA Pacific Northwest Natl Lab Phys Sci Div Phys & Computat Sci Directorate Richland WA 99354 USA Oak Ridge Natl Lab Ctr Nanophase Mat Sci Oak Ridge TN 37831 USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37831 USA

The application of machine learning is demonstrated for rapid and accurate extraction of plasmonic particles cluster geometries from hyperspectral image data via a dual variational autoencoder (dual-VAE). In this approach, the information is shared between the latent spaces of two VAEs acting on the particle shape data and spectral data, respectively, but enforcing a common encoding on the shape-spectra pairs. It is shown that this approach can establish the relationship between the geometric characteristics of nanoparticles and their far-field photonic responses, demonstrating that hyperspectral darkfield microscopy can be used to accurately predict the geometry (number of particles, arrangement) of a multiparticle assemblies below the diffraction limit in an automated fashion with high fidelity (for monomers (0.96), dimers (0.86), and trimers (0.58). This approach of building structure-property relationships via shared encoding is universal and should have applications to a broader range of materials science and physics problems in imaging of both molecular and nanomaterial systems.

关键词： darkfield scattering spectra machine learning plasmonic gold particles scanning electron microscopy structure-property prediction variational autoencoder

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Generating various airfoils with required lift coefficients by combining NACA and Joukowski airfoils using conditional variational autoencoders

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期108卷 104560-104560页

作者： Yonekura, Kazuo Wada, Kazunari Suzuki, Katsuyuki Univ Tokyo Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan

The aim of inverse design problem in mechanical design is to obtain shapes that satisfy the required design specifications. It is also desired to obtain varieties of shapes, but finding multiple shapes in a short computation time is difficult using the conventional methods. This paper proposes the use of the conditional variational autoencoders (CVAE) with normal distribution, denoted by N-CVAE, along with the von Mises-Fischer distribution, denoted by S-CVAE, to find multiple solutions for the inverse design problems. Both the CVAE models embed shapes into a latent space. The S-CVAE enables the separation of data in the latent space, whereas the N-CVAE embeds the data in a narrow space. These different features are used for various tasks in this study. In the first task, the dataset consists of only one type of data. Here, S-CVAE outperforms N-CVAE because it can easily separate the data. In the second task, the dataset consists of two different types of airfoils. It is desired to combine two types and to generate new types of data. N-CVAE is useful in this task since it embeds different shapes in the same latent area, due to which, the model outputs intermediate shapes of different types. The shape-generation capability of S-CVAE and N-CVAE are experimentally compared in this study.

关键词： Design exploration Inverse problem variational autoencoder Airfoil design

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Predicting new drug indications based on double variational autoencoders

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COMPUTERS IN BIOLOGY AND MEDICINE 2023年第1期164卷 107261-107261页

作者： Huang, Zhaoyang Chen, Shengjian Yu, Liang Xidian Univ Sch Comp Sci & Technol Xian 710071 Shaanxi Peoples R China

Experimental drug development is costly, complex, and time-consuming, and the number of drugs that have been put into application treatment is small. The identification of drug-disease correlations can provide important information for drug discovery and drug repurposing. Computational drug repurposing is an important and effective method that can be used to determine novel treatments for diseases. In recent years, an increasing number of large databases have been utilized for biological data research, particularly in the fields of drugs and diseases. Consequently, researchers have begun to explore the application of deep neural networks in biological data development. One particularly promising method for unsupervised learning is the deep generative model, with the variational autoencoder (VAE) being among the mainstream models. Here, we propose a drug indication prediction algorithm called DIDVAE (predicting new drug indications based on double variational autoencoders), which generates new data by learning the latent variable distribution of known data to achieve the goal of predicting drug-disease associations. In the experiment, we compared the DIDVAE algorithm with the BBNR, DrugNet, MBiRW and DRRS algorithms on a unified dataset. The comprehensive experimental results show that, compared with these prediction algorithms, the DIDVAE algorithm provides an overall improved prediction. In addition, further analysis and verification of the predicted unknown drug-disease association also proved the practicality of the method.

关键词： Drug repurposing Drug indications Generative model variational autoencoder

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