Anomaly Detection and Identification Method for Shield Tunneling Based on Energy Consumption Perspective by Min Hu, Fan Zhang, Huiming Wu

“…The AD_SI model first monitors the shield machine’s energy consumption status based on the VAE-LSTM (Variational Autoencoder–Long Short-Term Memory) algorithm with a dynamic threshold, thereby detecting abnormal sections. …”
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An approach using ddRADseq and machine learning for understanding speciation in Antarctic Antarctophilinidae gastropods by Juan Moles, Shahan Derkarabetian, Stefano Schiaparelli, Michael Schrödl, Jesús S. Troncoso, Nerida G. Wilson, Gonzalo Giribet

“…Novel approaches to identify distinctive genetic lineages, including unsupervised machine learning variational autoencoder plots, were used to establish species hypothesis frameworks. …”
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Natural scene reconstruction from fMRI signals using generative latent diffusion by Furkan Ozcelik, Rufin VanRullen

“…In the first stage, starting from fMRI signals, we reconstruct images that capture low-level properties and overall layout using a VDVAE (Very Deep Variational Autoencoder) model. In the second stage, we use the image-to-image framework of a latent diffusion model (Versatile Diffusion) conditioned on predicted multimodal (text and visual) features, to generate final reconstructed images. …”
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End-to-End Path Planning Under Linear Temporal Logic Specifications by Chaeeun Yang, Sojeong Yoon, Kyunghoon Cho

“…Key to our framework is the use of a Conditional Variational Autoencoder (CVAE), which is adept at identifying the optimal distribution of trajectories. …”
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An Empirical Evaluation of Deep Learning for Network Anomaly Detection by Ritesh K. Malaiya, Donghwoon Kwon, Sang C. Suh, Hyunjoo Kim, Ikkyun Kim, Jinoh Kim

“…In this study, we design and examine deep learning models constructed based on Fully Connected Networks (FCNs), Variational AutoEncoder (VAE), and Sequence-to-Sequence (Seq2Seq) structures. …”
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Soft Sensor for Ethanol Fermentation Monitoring through Data-Driven Modeling and Synthetic Data Generation by Hyun Kwon, Joseph Shiu, Elmer Ccopa Rivera, Celina Yamakaya

“…Through the use of a variational autoencoder (VAE), synthetic time series data was successfully generated, facilitating training and testing of a deep neural network on both original and synthetic datasets. …”
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Anomaly Detection for Agricultural Vehicles Using Autoencoders by Esma Mujkic, Mark P. Philipsen, Thomas B. Moeslund, Martin P. Christiansen, Ole Ravn

“…Basic autoencoder (AE), vector-quantized variational autoencoder (VQ-VAE), denoising autoencoder (DAE) and semisupervised autoencoder (SSAE) with a max-margin-inspired loss function are investigated and compared with a baseline object detector based on YOLOv5. …”
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CLOUD-SMART SURVEILLANCE: ENHANCING ANOMALY DETECTION IN VIDEO STREAMS WITH DF-CONVLSTM-BASED VAE-GAN by Sivalingan H

“…This paper proposes a novel solution: the double-flow convolutional Long Short-Term Memory (DF-ConvLSTM) - based Variational Autoencoder- Generative Adversarial Network (VAE-GAN) method. …”
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On the use of clustering workflows for automated microstructure segmentation of analytical STEM datasets by Zhiquan Kho, Andy Bridger, Keith Butler, Ercin C. Duran, Mohsen Danaie, Alexander S. Eggeman

“…It was found that the cluster output of a variational autoencoder (VAE) performed better compared to a more conventional latent transformation via Uniform Manifold Approximation & Projection (UMAP) on 4D-STEM data alone. …”
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Learning to draw vector graphics : applying generative modeling to font glyphs by Zhong, Kimberli

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Inorganic Materials Synthesis Planning with Literature-Trained Neural Networks by Kim, Edward, Jensen, Zach, van Grootel, Alexander, Huang, Kevin Joon-Ming, Staib, Matthew, Mysore, Sheshera, Chang, Haw-Shiuan, Strubell, Emma, McCallum, Andrew, Jegelka, Stefanie Sabrina, Olivetti, Elsa A.

“…Starting from the natural language text, we apply word embeddings from language models, which are fed into a named entity recognition model, upon which a conditional variational autoencoder is trained to generate syntheses for any inorganic materials of interest. …”
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The Variational Homoencoder: Learning to learn high capacity generative models from few examples by Hewitt, Luke B., Nye, Maxwell I., Gane, Andreea, Jaakkola, Tommi, Tenenbaum, Joshua B.

“…To address this, we develop a modification of the Variational Autoencoder in which encoded observations are decoded to new elements from the same class. …”
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Data augmentation guided breast cancer diagnosis and prognosis using an integrated deep-generative framework based on breast tumor’s morphological information by Muhammad Sakib Khan Inan, Sohrab Hossain, Mohammed Nazim Uddin

“…To that end, this study investigates the potentiality of deep generative models including, the tabular variational autoencoder (TVAE) and the conditional generative adversarial network (CTGAN), to generate high-quality synthetic tabular data of breast tumors and support the diagnosis and prognosis of breast cancer. …”
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Unsupervised machine learning discovery of structural units and transformation pathways from imaging data by Sergei V. Kalinin, Ondrej Dyck, Ayana Ghosh, Yongtao Liu, Bobby G. Sumpter, Maxim Ziatdinov

“…With only these postulates, we developed a machine learning method leveraging a rotationally invariant variational autoencoder (VAE) that can identify the existing molecular fragments observed within a material. …”
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Gas Turbine Anomaly Detection under Time-Varying Operation Conditions Based on Spectra Alignment and Self-Adaptive Normalization by Dongyan Miao, Kun Feng, Yuan Xiao, Zhouzheng Li, Jinji Gao

“…Degressive beta variational autoencoder is employed for learning spectra characteristics and anomaly detection, while a multi-category anomaly index is proposed to accommodate various operating conditions. …”
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Few-Shot User-Adaptable Radar-Based Breath Signal Sensing by Gianfranco Mauro, Maria De Carlos Diez, Julius Ott, Lorenzo Servadei, Manuel P. Cuellar, Diego P. Morales-Santos

“…Episodically, a convolutional variational autoencoder learns how to map the processed radar data to a reference signal, generating a constrained latent space to the central respiration frequency. …”
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Quantifying Uncertainties in the Quiet‐Time Ionosphere‐Thermosphere Using WAM‐IPE by Weijia Zhan, Alireza Doostan, Eric Sutton, Tzu‐Wei Fang

“…Ensemble simulations of the coupled Whole Atmosphere Model with Ionosphere Plasmasphere Electrodynamics (WAM‐IPE) driven by synthetic solar wind drivers generated through a multi‐channel variational autoencoder (MCVAE) model are obtained. Applying the polynomial chaos expansion (PCE) technique, it is possible to estimate the means and variances of the QoIs as well as the sensitivities of the QoIs with regard to the drivers. …”
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Semantic Decomposition and Anomaly Detection of Tympanic Membrane Endoscopic Images by Dahye Song, In Sik Song, Jaeyoung Kim, June Choi, Yeonjoon Lee

“…To solve these limitations, we studied anomaly detection, the task of identifying sample data that do not match the overall data distribution with the Variational Autoencoder (VAE), an unsupervised learning model. …”
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Exploring VAE-driven implicit parametric unit cells for multiscale topology optimization by Chenchen Chu, Alexander Leichner, Franziska Wenz, Heiko Andrä

“…Subsequently, we utilize a Variational Autoencoder (VAE), integrated with a regressor, to train on this diversified database. …”
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AI nutrition recommendation using a deep generative model and ChatGPT by Ilias Papastratis, Dimitrios Konstantinidis, Petros Daras, Kosmas Dimitropoulos

“…The use of a variational autoencoder to robustly model the anthropometric measurements and medical condition of users in a descriptive latent space, as well as the use of an optimizer to adjust meal quantities based on users’ energy requirements enable the proposed method to generate highly accurate, nutritious and personalized weekly meal plans. …”
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