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

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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ALCR: Adaptive loss based critic ranking toward variational autoencoders with multinomial likelihood and condition for collaborative filtering

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KNOWLEDGE-BASED SYSTEMS 2023年第1期278卷

作者： Feng, Jiamei Liu, Mengchi Liang, Xiang Nie, Tingkun South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China

Research on variational autoencoders for collaborative filtering is gradually focusing on implicit feedback. However, most existing studies have two limitations: (1) they overlook the impact of user- item interaction data in implicit feedback on the representations of both users and items, which can affect the latent representations;(2) their attention is mainly focused on the immediate feedback of recommended items, ignoring interactions between feedback and ground-truth values, and neglecting the difference on loss functions between different training processes. To address these limitations, we first propose a condition for variational autoencoders to control user and item representations to learn more useful information from the latent representations. Then, we train an adaptive loss critic ranking to directly provide ranking scores in collaborative filtering recommendations, which aims to minimize loss and improve interactions during different critic training processes. Extensive experiments on three big real-world social media datasets demonstrate that this approach outperforms the existing twelve models under NDCG and Recall metric estimation settings and significantly improves the performance of a variety of prediction models.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Collaborative filtering variational autoencoder Rank Actor-critic Adaptive loss

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Anomaly detection in Fourier transform infrared spectroscopy of geological specimens using variational autoencoders

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ORE GEOLOGY REVIEWS 2023年 158卷

作者： Gonzalez, C. M. Horrocks, T. Wedge, D. Holden, E. J. Hackman, N. Green, T. Univ Western Australia Ctr Data driven Geosci Sch Earth Sci Crawley WA 6009 Australia Rio Tinto Iron Ore Perth WA 6000 Australia

Fourier Transform infrared spectroscopy (FTIR) is an emerging cost effective and rapid mineralogical charac-terization technique being applied in the geosciences. Detecting anomalous FTIR spectra is especially relevant to the geoscience domain, as it may indicate abrupt changes in geology or mineralogical composition of the rock sample being examined. Given a large volume of data, detecting anomalies that exhibit significant and abrupt spatial and compositional variability is a time-consuming and challenging task. This paper explores the use of an unsupervised variational autoencoder (VAE) for determining anomalies that may exist within a set of FTIR spectra collected from reverse circulation (RC) drill chip samples spanning several iron ore deposits from the Pilbara region in Western Australia. Diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) were measured from 1,579 two-metre composite samples. Our results showed that the VAE was effective in separating anomalous spectra from spectra typical of unmineralized banded iron formation by leveraging the probabilistic latent representation of the spectra in as few as two latent dimensions. To validate our results, detected anomalous samples were compared with their respective geochemical assays to analyse their mineralogical differences, which may have led to the anomalous spectra. In the iron ore sample data used in this study, the observed spectral anomalies were shown to have elevated concentrations of Al2O3 and TiO2 wt.% while being several standard deviations below the mean Fe2O3 wt.% indicating mineralogies rich in shale as opposed to iron oxide rich mineralogies. While the paper demonstrates the efficacy of the VAE in anomaly detection, it can also be effective in assuring the quality of the FTIR data as a pre-processing step, which is critically important for machine learning applications.

关键词： variational autoencoder Fourier transform infrared spectroscopy Anomaly detection Iron ore Pilbara

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Gated Mixture variational autoencoders for Value Added Tax audit case selection

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KNOWLEDGE-BASED SYSTEMS 2020年 188卷 105048-105048页

作者： Kleanthous, Christos Chatzis, Sotirios Cyprus Univ Technol Dept Elect Engn Comp Engn & Informat CY-3036 Limassol Cyprus Cyprus Tax Dept Nicosia Cyprus

In this work, we address the problem of targeted Value Added Tax (VAT) audit case selection by means of machine learning. This is a challenging problem that has remained rather elusive for EU-based Tax Departments, due to the inadequate quantity of tax audits that can be used for conventional supervised model training. To this end, we devise a novel Gated Mixture variational autoencoder deep network, that can be effectively trained with data from a limited number of audited taxpayers, combined with a large corpus of filed VAT returns. This gives rise to a semi-supervised learning framework that leverages the latest advances in deep learning and robust regularization using variational inference. We developed our approach in collaboration with the Cyprus Tax Department and experimentally deployed it to facilitate its audit selection process;to this end, we used actual VAT data from Cyprus-based taxpayers. This way, we obtained strong empirical evidence that our approach can greatly facilitate the VAT audit case selection process. Specifically, we obtained up to 76% out-of-sample accuracy in detecting whether a significant tax yield will be generated from a specific prospective VAT audit. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Value Added Tax Audit selection variational autoencoder Finite mixture model

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Expediting structure-property analyses using variational autoencoders with regression

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COMPUTATIONAL MATERIALS SCIENCE 2024年 242卷

作者： Templeton, William Frieden Miner, Justin P. Ngo, Austin Fitzwater, Lauren Reddy, Tharun Abranovic, Brandon Prichard, Paul Lewandowski, John J. Narra, Sneha Prabha Carnegie Mellon Univ Dept Mech Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Case Western Reserve Univ Dept Mat Sci & Engn 10900 Euclid Ave Cleveland OH 44106 USA Carnegie Mellon Univ Dept Mat Sci & Engn 5000 Forbes Ave Pittsburgh PA 15213 USA Kennametal Inc 1600 Technol Way Latrobe PA 15650 USA

We present a machine learning approach that expedites structure-property analysis in materials, bypassing traditional feature extraction and exploratory data analysis techniques. This objective is accomplished by employing a variational autoencoder (VAE) structure that is modified to include a regressor network for property prediction (VAE-Regression). This modification allows for direct linkage of imaged features and quantitative part properties within the VAE latent space. We first demonstrate our approach using 2D optical micrographs and corresponding four -point bend fatigue life data from laser beam powder bed fusion additively manufactured Ti-6Al-4V coupons. The VAE-Regression model extracts spatial features, predicts fatigue life, and identifies features of porosity defect governing fatigue behavior such as pore clusters, pores near sample edges, and jagged pore morphologies. These features corroborate fatigue literature on physics -based modeling and experimentation. We then demonstrate the versatility of our methodology using binder jet additively manufactured WC -Co coupons, where porosity and microstructural discontinuities are known to lower the three-point bend transverse rupture strength, but the interaction between the WC and Co are yet to be completely understood. We attempted to understand these interactions using our VAE-Regression architecture. Within our dataset, we show that coarser WC grains surrounded by larger Co pools indicate lower strength, while finer WC grains with smaller Co pools indicate higher strength. This machine learning approach using image -based data will likely prove to be critical in understanding and identifying structure-property relationships in new materials and manufacturing processes.

关键词： variational autoencoder Probabilistic modeling Additive manufacturing Regression structure-property relationship Feature vector representation

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Achieving explainability for plant disease classification with disentangled variational autoencoders

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartA期133卷

作者： Habaragamuwa, Harshana Oishi, Yu Tanaka, Kenichi Natl Agr & Food Res Org NARO Tsukuba Japan Natl Agr & Food Res Org Res Ctr Agr Informat Technol Core Technol Res Headquarters 1-31-1 Kannondai Tsukuba Ibaraki 3050856 Japan

Agricultural image recognition tasks are becoming increasingly dependent on systems based on deep learning (DL);however, despite the excellent performance of DL, it is difficult to comprehend the type of logic or features of the input image it uses during decision making. Knowing the logic or features is highly crucial for result verification, algorithm improvement, training data improvement, and knowledge extraction. However, the explanations from the current heatmap-based algorithms are insufficient for the abovementioned requirements. To address this, this paper details the development of a classification and explanation method based on a variational autoencoder (VAE) architecture, which can visualize the variations of the most important features by visualizing the generated images that correspond to the variations of those features. Using the PlantVillage dataset, an acceptable level of explainability was achieved without sacrificing the classification accuracy. The proposed method can also be extended to other crops as well as other image classification tasks. Further, application systems using this method for disease identification tasks, such as the identification of potato blackleg disease, potato virus Y, and other image classification tasks, are currently being developed.

关键词： Explainable deep learning variational autoencoder Feature visualization Disentangled representation Crop disease classification

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Generating In-Between Images Through Learned Latent Space Representation Using variational autoencoders

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IEEE ACCESS 2020年 8卷 149456-149467页

作者： Cristovao, Paulino Nakada, Hidemoto Tanimura, Yusuke Asoh, Hideki Univ Tsukuba Dept Compute Sci Tsukuba Ibaraki 3058577 Japan Natl Inst Adv Ind Sci & Technol Tsukuba Ibaraki 3058560 Japan

Image interpolation is often implemented using one of two methods: optical flow or convolutional neural networks. These methods are typically pixel-based;they do not work well on objects between images far apart. Because they either rely on a simple frame average or pixel motion, they do not have the required knowledge of the semantic structure of the data. In this paper, we propose a method for image interpolation based on latent representations. We use a simple network structure based on a variational autoencoder and an adjustable hyperparameter that imposes the latent space distribution to generate accurate interpolation. To visualize the effects of the proposed approach, we evaluate a synthetic dataset. We demonstrate that our method outperforms both pixel-based methods and a conventional variational autoencoder, with particular improvements in nonsuccessive images.

关键词： Interpolation Optical imaging Optical computing Semantics Optical noise Aerospace electronics Task analysis Image interpolation latent variables representation learning variational autoencoder

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