Trustworthy Inverse Molecular Design via Alignment with Molecular Dynamics by Kevin Tirta Wijaya, Navid Ansari, Hans‐Peter Seidel, Vahid Babaei

“…TrustMol first constructs a latent space with a novel variational autoencoder (VAE) and trains an ensemble of property predictors to learn the mapping from the latent space to the property space. …”
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Dirichlet latent modelling enables effective learning and sampling of the functional protein design space by Evgenii Lobzaev, Giovanni Stracquadanio

“…Here we propose an autoregressive model, called Temporal Dirichlet Variational Autoencoder (TDVAE), which exploits the mathematical properties of the Dirichlet distribution and temporal convolution to efficiently learn high-order information from a functionally related, possibly remotely similar, set of sequences. …”
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A generative model-based coevolutionary training framework for noise-tolerant softsensors in wastewater treatment processes by Yu Peng, Erchao Li

“…The framework employs a denoising variational autoencoder to extract global and local features from auxiliary data, enhancing population distribution and constructing a deep nonlinear representation to counter noise effects. …”
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Research on image generation technology based on deep learning by Li Jinchen

“…This paper mainly introduces two main methods: generating adversarial network (GAN) and variational autoencoder (VAE). GAN has been widely used in image generation, image repair and other aspects. …”
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Generative Local Interpretable Model-Agnostic Explanations by Mohammad Nagahisarchoghaei, Mirhossein Mousavi Karimi, Shahram Rahimi, Logan Cummins, Ghodsieh Ghanbari

“…In this paper, we aim to improve LIME and SHAP by using a pre-trained Variational AutoEncoder (VAE) on the training dataset to generate realistic data around the test instance. …”
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A Trust-By-Learning Framework for Secure 6G Wireless Networks Under Native Generative AI Attacks by Md Shirajum Munir, Sravanthi Proddatoori, Manjushree Muralidhara, Trinidad Mario Dena, Walid Saad, Zhu Han, Sachin Shetty

“…Extensive experimental analysis demonstrates that native GenAI methods, such as generative adversarial network (GAN), variational autoencoder (VAE), and autoencoder have significant capability to enforce poisoning attacks. …”
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Inverse design of catalytic active sites via interpretable topology-based deep generative models by Bingxu Wang, Shisheng Zheng, Jie Wu, Jingyan Li, Feng Pan

“…Herein, we developed a topology-based variational autoencoder framework (PGH-VAEs) to enable the interpretable inverse design of catalytic active sites. …”
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DOLPHIN advances single-cell transcriptomics beyond gene level by leveraging exon and junction reads by Kailu Song, Yumin Zheng, Bowen Zhao, David H. Eidelman, Jian Tang, Jun Ding

“…These graphs are processed by a variational graph autoencoder to improve cell embeddings. …”
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Optimizing drug design by merging generative AI with a physics-based active learning framework by Isaac Filella-Merce, Alexis Molina, Lucía Díaz, Marek Orzechowski, Yamina A. Berchiche, Yang Ming Zhu, Júlia Vilalta-Mor, Laura Malo, Ajay S. Yekkirala, Soumya Ray, Victor Guallar

“…To address these, we developed a GM workflow integrating a variational autoencoder with two nested active learning cycles. …”
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Social-aware trajectory prediction using goal-directed attention networks with egocentric vision by Lia Astuti, Chui-Hong Chiu, Yu-Chen Lin, Ming-Chih Lin

“…A graph attention network (GAT) captures socially-aware interaction correlation, a long short-term memory (LSTM) network encodes temporal dependencies, a goal-directed forecaster (GDF) estimates coarse future goals, and a conditional variational autoencoder (CVAE) generates multiple plausible trajectories, with multi-head attention (MHA) and feed-forward networks (FFN) refining the final multimodal trajectory prediction. …”
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Research on intelligent routing with VAE-GAN in the internet of body. by Song Qian, Tianping Zhang, Siping Hu

“…This paper proposes an artificial intelligence routing algorithm, combines the variational autoencoder (VAE) and the generative adversarial network model (GAN) to construct a VAE-GAN model to generate multiple sets of data to achieve data enhancement on the Internet of Body. …”
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Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions by J. Quetzalcóatl Toledo-Marín, Sebastian Gonzalez, Hao Jia, Ian Lu, Deniz Sogutlu, Abhishek Abhishek, Colin Gay, Eric Paquet, Roger G. Melko, Geoffrey C. Fox, Maximilian Swiatlowski, Wojciech Fedorko

“…To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). …”
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Elevator fault precursor prediction based on improved LSTM-AE algorithm and TSO-VMD denoising technique. by Hao Cao, Xiaoyan Du

“…This study proposes an advanced elevator fault precursor prediction method integrating Variational Mode Decomposition (VMD), Bidirectional Long Short-Term Memory (BILSTM), and an Autoencoder with an Attention Mechanism (AEAM), collectively referred to as the VMD-BILSTM-AEAM algorithm. …”
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Cold-start visualization recommendation driven by large language models for ocean data analysis by Xin Li, Jixiu Liao, Wen Liu, Yu Miao, Leyu Wang, Shuqing Sun

“…Moreover, although existing interaction methods are diverse, their precise control fails to effectively convey users' ambiguous intentions.MethodsTo address these issues, we introduce a novel cold-start visualization recommendation system that integrates a Large Language Model (LLM) and a Grammar Variational Autoencoder (GVAE). The LLM generates initial exploratory goals and visualization recommendations based on data descriptions, while the GVAE produces visual summary projections to verify the extent of user intent fulfillment. …”
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Temporal-Sequence Offline Reinforcement Learning for Transition Control of a Novel Tilt-Wing Unmanned Aerial Vehicle by Shiji Jin, Wenjie Zhao

“…The policy is further constrained within an offline dataset collected via hardware-in-the-loop simulation using a variational autoencoder, and a sequence-level prediction mechanism is introduced to ensure temporal consistency across action trajectories, thereby mitigating extrapolation error while preserving data fidelity. …”
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Tackling multimodal device distributions in inverse photonic design using invertible neural networks by Michel Frising, Jorge Bravo-Abad, Ferry Prins

“…We compare a commonly used conditional variational autoencoder (cVAE) and a conditional invertible neural network (cINN) on a proof-of-principle nanophotonic problem, consisting in tailoring the transmission spectrum trough a metallic film milled by subwavelength indentations. …”
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LatentDE: latent-based directed evolution for protein sequence design by Thanh V T Tran, Nhat Khang Ngo, Viet Thanh Duy Nguyen, Truong-Son Hy

“…At its core, LDE is a regularized variational autoencoder (VAE), harnessing the capabilities of the state-of-the-art protein language model, ESM-2, to construct a meaningful latent space of sequences. …”
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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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Clustering cell nuclei on microgrooves for disease diagnosis using deep learning by Bettina Roellinger, Francois Thenier, Claire Leclech, Catherine Coirault, Elsa Angelini, Abdul I. Barakat

“…Here we assess the ability of a Variational Autoencoder (VAE) and a Gaussian Mixture Model (GMM) to cluster patches of nuclei of both wildtype myoblasts and myoblasts with laminopathy-associated mutations cultured on microgroove substrates, and we explore the impact of image processing parameters on clustering performance. …”
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A Customizable Face Generation Method Based on Stable Diffusion Model by Wenlong Xiang, Shuzhen Xu, Cuicui Lv, Shuo Wang

“…In addition, we modify the structure of Variational Autoencoder to enhance generation efficiency. …”
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