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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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Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第4期4卷 045033页

作者： Ziatdinov, Maxim Wong, Chun Yin (Tommy) Kalinin, Sergei, V 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 Univ Tennessee Bredesen Ctr Interdisciplinary Res & Grad Educ Knoxville TN 37996 USA Univ Tennessee Dept Mat Sci & Engn Knoxville TN 37996 USA

Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, t

关键词： variational autoencoder scanning tunneling microscopy scanning transmission electron microscopy unsupervised learning latent representation

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Using shapley values and variational autoencoders to explain predictive models with dependent mixed features

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 9553-9603页

作者： Lars Henry Berge Olsen Ingrid Kristine Glad Martin Jullum Kjersti Aas Department of Mathematics University of Oslo Oslo Norway Norwegian Computing Center 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 theoretical 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 dependencies 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 methods. 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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Combining variational autoencoders and physical bias for improved microscopy data analysis

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MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2023年第4期4卷 045004页

作者： Biswas, Arpan Ziatdinov, Maxim Kalinin, Sergei, V Ctr Nanophase Mat Sci Oak Ridge Natl Lab Oak Ridge TN 37831 USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37831 USA Univ Tennessee Mat Sci & Engn Knoxville TN 37996 USA

Electron and scanning probe microscopy produce vast amounts of data in the form of images or hyperspectral data, such as electron energy loss spectroscopy or 4D scanning transmission electron microscope, that contain information on a wide range of structural, physical, and chemical properties of materials. To extract valuable insights from these data, it is crucial to identify physically separate regions in the data, such as phases, ferroic variants, and boundaries between them. In order to derive an easily interpretable feature analysis, combining with well-defined boundaries in a principled and unsupervised manner, here we present a physics augmented machine learning method which combines the capability of variational autoencoders to disentangle factors of variability within the data and the physics driven loss function that seeks to minimize the total length of the discontinuities in images corresponding to latent representations. Our method is applied to various materials, including NiO-LSMO, BiFeO3, and graphene. The results demonstrate the effectiveness of our approach in extracting meaningful information from large volumes of imaging data.

关键词： ferroic variants physics driven loss variational autoencoder latent space unsupervised learning

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Integrating Adversarial Generative Network with variational autoencoders towards Cross-Modal Alignment for Zero-Shot Remote Sensing Image Scene Classification

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REMOTE SENSING 2022年第18期14卷 4533页

作者： Ma, Suqiang Liu, Chun Li, Zheng Yang, Wei Henan Univ Sch Comp & Informat Engn Kaifeng 475000 Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng 475004 Peoples R China Henan Univ Henan Engn Lab Spatial Informat Proc Kaifeng 475004 Peoples R China Henan Univ Henan Ind Technol Acad Spatio Temporal Big Data Zhengzhou 450046 Peoples R China

Remote sensing image scene classification takes image blocks as classification units and predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples for all classes of remote sensing image scenes, zero-shot classification methods which can recognize image scenes that are not seen in the training stage are of great significance. By projecting the image visual features and the class semantic features into the latent space and ensuring their alignment, the variational autoencoder (VAE) generative model has been applied to address remote-sensing image scene classification under a zero-shot setting. However, the VAE model takes the element-wise square error as the reconstruction loss, which may not be suitable for measuring the reconstruction quality of the visual and semantic features. Therefore, this paper proposes to augment the VAE models with the generative adversarial network (GAN) to make use of the GAN's discriminator in order to learn a suitable reconstruction quality metric for VAE. To promote feature alignment in the latent space, we have also proposed cross-modal feature-matching loss to make sure that the visual features of one class are aligned with the semantic features of the class and not those of other classes. Based on a public dataset, our experiments have shown the effects of the proposed improvements. Moreover, taking the ResNet models of ResNet18, extracting 512-dimensional visual features, and ResNet50 and ResNet101, both extracting 2048-dimensional visual features for testing, the impact of the different visual feature extractors has also been investigated. The experimental results show that better performance is achieved by ResNet18. This indicates that more layers of the extractors and larger dimensions of the extracted features may not contribute to the image scene classification under a zero-shot setting.

关键词： zero-shot learning remote sensing image scene classification cross-modal feature alignment variational autoencoder generative adversarial network

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