High‐dimensional Bayesian optimization for metamaterial design by Zhichao Tian, Yang Yang, Sui Zhou, Tian Zhou, Ke Deng, Chunlin Ji, Yejun He, Jun S. Liu

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Exploration of Generative Neural Networks for Police Facial Sketches by Nerea Sádaba-Campo, Hilario Gómez-Moreno

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A systematic review of deep learning chemical language models in recent era by Hector Flores-Hernandez, Emmanuel Martinez-Ledesma

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Exploring the Frontier: Generative AI Applications in Online Consumer Behavior Analytics by Takuma Kimura

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Efficient and Fast Light Field Compression via VAE-Based Spatial and Angular Disentanglement by Soheib Takhtardeshir, Roger Olsson, Christine Guillemot, Marten Sjostrom

“…However, the efficient processing of this data, particularly in terms of compression and encoding/decoding time, presents challenges. We propose a Variational Autoencoder (VAE)-based framework to disentangle the spatial and angular features of light field images, focusing on fast and efficient compression. …”
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Autoencoder Reconstruction of Cosmological Microlensing Magnification Maps by Somayeh Khakpash, Federica B. Bianco, Georgios Vernardos, Gregory Dobler, Charles Keeton

“…Enhanced modeling of microlensing variations in light curves of strongly lensed quasars improves measurements of cosmological time delays, the Hubble Constant, and quasar structure. …”
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Cosmology with One Galaxy: Autoencoding the Galaxy Properties Manifold by Amanda Lue, Shy Genel, Marc Huertas-Company, Francisco Villaescusa-Navarro, Matthew Ho

“…In contrast, variations in other parameters such as σ _8 cause negligible error changes, suggesting galaxies shift along the manifold. …”
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Advancing Agile Software Cost Estimation Through Data Synthesis: A Comparative Analysis of Five Generation Techniques by Xiaoyan Zhao, Zulkefli Mansor, Rozilawati Razali, Mohd Zakree Ahmad Nazri, Xin Xiong

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Predicting proprioceptive cortical anatomy and neural coding with topographic autoencoders. by Max Grogan, Kyle P Blum, Yufei Wu, J Alex Harston, Lee E Miller, A Aldo Faisal

“…We developed a topographic variational autoencoder with lateral connectivity (topo-VAE) to compute a putative cortical map from a large set of natural movement data. …”
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From KL Divergence to Wasserstein Distance: Enhancing Autoencoders with FID Analysis by Laxmi Kanta Poudel, Kshtiz Aryal, Rajendra Bahadur Thapa, Sushil Poudel

“… Variational Autoencoders (VAEs) are popular Bayesian inference models that excel at approximating complex data distributions in a lower-dimensional latent space. …”
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Efficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics data by Amirhossein D Naghdi, Grzegorz Kaszuba, Stefanos Papanikolaou, Andrzej Jaszkiewicz, Piotr Sankowski

“…We employ high-quality interatomic potentials to create a dataset of NiFeCr structures and apply crystal diffusion variational autoencoder to maximize their mechanical properties, i.e. bulk modulus. …”
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Anomaly detection of smart grid stealing network attacks based on deep autoencoder by Huang Yan, Li Jincan, Yang Xiaqin, Li Pei, Li Zi

“…The performance of simple autoencoders, variational autoencoders, and Attention Enhanced Autoencoders (AEA) was studied, revealing that using the seq2seq structure in these three types of autoencoders results in superior detection performance compared to fully connected architectures. …”
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Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism by Yikai Huang, Jinchuan Tang, Shuping Dang

“…Graph autoencoders’ inherent capability to capture node feature correlations poses significant privacy risks through attackers inference. …”
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Combined compression and encryption of linear wireless sensor network data using autoencoders by N. Shylashree, Sachin Kumar, Hong Min

“…When the sensors are deployed closely and with a gradual variation of sensor data along the route, a high degree of correlation exists among the sensed data. …”
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Enhancing Anomaly Detection Through Latent Space Manipulation in Autoencoders: A Comparative Analysis by Tomasz Walczyna, Damian Jankowski, Zbigniew Piotrowski

“…A comparative analysis of autoencoders, Variational Autoencoders, and their modified counterparts was conducted within a tailored experimental environment designed to simulate real-world scenarios. …”
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Out-of-Roundness Wheel Damage Identification in Railway Vehicles Using AutoEncoder Models by Renato Melo, Rafaelle Finotti, António Guedes, Vítor Gonçalves, Andreia Meixedo, Diogo Ribeiro, Flávio Barbosa, Alexandre Cury

“…This study presents a comparative analysis of three AutoEncoder (AE) models—Variational AutoEncoder (VAE), Sparse AutoEncoder (SAE), and Convolutional AutoEncoder (CAE)—to detect and quantify structural anomalies in railway vehicle wheels, such as polygonization. …”
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Design of an Iterative Method for Malware Detection Using Autoencoders and Hybrid Machine Learning Models by Rijvan Beg, R. K. Pateriya, Deepak Singh Tomar

“…Expanding the applicability of the framework, we use semi-supervised self-training using Variational Autoencoders (VAEs) to use both labeled and unlabeled datasets & samples. …”
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Autoencoder-Driven Fiducial Landmark Identification in 3D Brain MRI for Neuroimaging Alignment by G. Deepali, H. Anitha, B. P. Swathi, M. V. Suhas

“…However, manual annotation of these landmarks is time-consuming, prone to human error, and further complicated by variations in MRI acquisition protocols and incomplete head coverage. …”
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Outlier Detection and Correction in Smart Grid Energy Demand Data Using Sparse Autoencoders by Levi da Costa Pimentel, Ricardo Wagner Correia Guerra Filho, Juan Moises Mauricio Villanueva, Yuri Percy Molina Rodriguez

“…This work develops an integrated methodology for the detection and correction of outliers in energy demand data, based on Artificial Neural Network autoencoders. The proposed approach is submitted across multiple scenarios using real-world data from a substation, where the influence of the variation in the number of outliers present in the database is evaluated, as well as the variation in their amplitudes on the functioning of the algorithms. …”
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DNAE-GAN: Noise-free acoustic signal generator by integrating autoencoder and generative adversarial network by Ping-Huan Kuo, Ssu-Ting Lin, Jun Hu

“…However, linear predictive coding–generated voices have limited variations and exhibit excessive noise. To resolve these problems, this article proposes an artificial intelligence model that combines a denoise autoencoder with generative adversarial networks. …”
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