Multi-batch single-cell comparative atlas construction by deep learning disentanglement by Allen W. Lynch, Myles Brown, Clifford A. Meyer

“…Here we propose CODAL, a variational autoencoder-based statistical model which uses a mutual information regularization technique to explicitly disentangle factors related to technical and biological effects. …”
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Designing meaningful continuous representations of T cell receptor sequences with deep generative models by Allen Y. Leary, Darius Scott, Namita T. Gupta, Janelle C. Waite, Dimitris Skokos, Gurinder S. Atwal, Peter G. Hawkins

“…To address the current limitations in TCR analysis we develop a capacity-controlled disentangling variational autoencoder trained using a dataset of approximately 100 million TCR sequences, that we name TCR-VALID. …”
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Microstructure reconstruction of 2D/3D random materials via diffusion-based deep generative models by Xianrui Lyu, Xiaodan Ren

“…Confronting the limitations of variational autoencoder and generative adversarial network within generative models, this study adopted the denoising diffusion probabilistic model (DDPM) to learn the probability distribution of high-dimensional raw data and successfully reconstructed the microstructures of various composite materials, such as inclusion materials, spinodal decomposition materials, chessboard materials, fractal noise materials, and so on. …”
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Learning to drive as humans do: Reinforcement learning for autonomous navigation by Ge Lun, Zhou Xiaoguang, Wang Yongcong

“…In an innovative approach, we apply the variational autoencoder technique to extract latent vectors from high-quality images, reconstructing a new state space with vehicle state vectors. …”
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stDyer enables spatial domain clustering with dynamic graph embedding by Ke Xu, Yu Xu, Zirui Wang, Xin Maizie Zhou, Lu Zhang

“…We introduce stDyer, an end-to-end deep learning framework for spatial domain clustering in SRT data. stDyer combines Gaussian Mixture Variational AutoEncoder with graph attention networks to learn embeddings and perform clustering. …”
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Sensitivity-guided iterative parameter identification and data generation with BayesFlow and PELS-VAE for model calibration by Yi Zhang, Lars Mikelsons

“…The sensitivity analysis replaces manual intervention, the parameter identification is realized by BayesFlow allowing for uncertainty quantification, and the data generation with the physics-enhanced latent space variational autoencoder (PELS-VAE) between two iteration steps enables inference of weakly identifiable parameters. …”
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Generating experimentally unrelated target molecule-binding highly functionalized nucleic-acid polymers using machine learning by Jonathan C. Chen, Jonathan P. Chen, Max W. Shen, Michael Wornow, Minwoo Bae, Wei-Hsi Yeh, Alvin Hsu, David R. Liu

“…Here, the authors use experimental in vitro selection results to train a conditional variational autoencoder machine learning model that generates biopolymers with no apparent sequence similarity to experimentally derived examples, but that nevertheless bind the target molecule with similar potent binding affinity.…”
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Period-aggregated transformer for learning latent seasonalities in long-horizon financial time series. by Zhenyang Tang, Jinshui Huang, Denisa Rinprasertmeechai

“…The model integrates a variational autoencoder (VAE) with a period-to-period attention mechanism for multistep prediction in the financial time series. …”
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AI reveals insights into link between CD33 and cognitive impairment in Alzheimer's Disease. by Tamara Raschka, Meemansa Sood, Bruce Schultz, Aybuge Altay, Christian Ebeling, Holger Fröhlich

“…This work addresses these challenges by employing the recently published Variational Autoencoder Modular Bayesian Networks (VAMBN) method, which we here trained on combined clinical and patient level gene expression data while incorporating a disease focused knowledge graph. …”
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Learning-Based Path Planning Under Co-Safe Temporal Logic Specifications by Kyunghoon Cho

“…By leveraging the conditional variational autoencoder, we learn the ideal search tree extension distribution in a given situation, which increases solution search efficiency. …”
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A convolutional neural networks based approach for clustering of emotional elements in art design by Xue Rui

“…Subsequently, these features are utilized to cluster emotional elements using a variational autoencoder (VAE). Through this clustering process, the poster images are categorized into positive, negative, and neutral classes. …”
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Evaluation of Natural Image Generation and Reconstruction Capabilities Based on the β-VAE Model by Zhang Honghao

“…However, the Variational Autoencoder (VAE) has limitations in image quality and diversity, while β-VAE achieves a balance between the decoupling of latent space and generative quality by adjusting the coefficient β. …”
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A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators by Mahindra Rautela, Alan Williams, Alexander Scheinker

“…CLARM consists of a Conditional Variational Autoencoder transforming six-dimensional phase space into a lower-dimensional latent distribution and a Long Short-Term Memory network capturing temporal dynamics in an autoregressive manner. …”
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Optimization of physical quantities in the autoencoder latent space by S. M. Park, H. G. Yoon, D. B. Lee, J. W. Choi, H. Y. Kwon, C. Won

“…Abstract We propose a strategy for optimizing physical quantities based on exploring in the latent space of a variational autoencoder (VAE). We train a VAE model using various spin configurations formed on a two-dimensional chiral magnetic system. …”
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Characterizing chromatin folding coordinate and landscape with deep learning. by Wen Jun Xie, Yifeng Qi, Bin Zhang

“…We applied a deep-learning approach, variational autoencoder (VAE), to analyze the fluctuation and heterogeneity of chromatin structures revealed by single-cell imaging and to identify a reaction coordinate for chromatin folding. …”
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Hybrid quantum-classical machine learning for generative chemistry and drug design by A. I. Gircha, A. S. Boev, K. Avchaciov, P. O. Fedichev, A. K. Fedorov

“…As the first step toward this goal, we built a compact discrete variational autoencoder (DVAE) with a Restricted Boltzmann Machine (RBM) of reduced size in its latent layer. …”
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SyntheVAEiser: augmenting traditional machine learning methods with VAE-based gene expression sample generation for improved cancer subtype predictions by Brian Karlberg, Raphael Kirchgaessner, Jordan Lee, Matthew Peterkort, Liam Beckman, Jeremy Goecks, Kyle Ellrott

“…We developed SyntheVAEiser, a variational autoencoder based tool that was trained and tested on over 8000 cancer samples. …”
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Deep Generative Model with Supervised Latent Space for Text Classification by Jankowski Maciej

“…We first start with the model known as Supervised Variational Autoencoder that is researched in the literature in various forms [3] and [4]. …”
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Novelty detection in rover-based planetary surface images using autoencoders by Braden Stefanuk, Krzysztof Skonieczny

“…In the domain of planetary science, novelty detection is gaining attention because of the operational opportunities it offers, including annotated data products and downlink prioritization. Using a variational autoencoder (VAE), this work improves upon state-of-the-art novelty detection performance in the context of Martian exploration by >7% (measured by the area under the receiver operating characteristic curve (ROC AUC)). …”
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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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