Self-Adaptive Evolutionary Info Variational Autoencoder per Toby A. Emm, Yu Zhang

Matèries: “…variational autoencoder…”
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Variational AutoEncoder for synthetic insurance data per Charlotte Jamotton, Donatien Hainaut

“…This article explores the application of Variational AutoEncoders (VAEs) to insurance data. Previous research has demonstrated the successful implementation of generative models, especially VAEs, across various domains, such as image recognition, text classification, and recommender systems. …”
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Generative autoencoder to prevent overregularization of variational autoencoder per YoungMin Ko, SunWoo Ko, YoungSoo Kim

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Nearest Neighbours Graph Variational AutoEncoder per Lorenzo Arsini, Barbara Caccia, Andrea Ciardiello, Stefano Giagu, Carlo Mancini Terracciano

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A DEBIASING VARIATIONAL AUTOENCODER FOR DEFORESTATION MAPPING per M. X. Ortega Adarme, M. X. Ortega Adarme, P. J. Soto Vega, G. A. O. P. Costa, R. Q. Feitosa, C. Heipke

“…In this work, we introduce an extension of the Debiasing Variational Autoencoder (DB-VAE) for semantic segmentation. …”
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Disentangled Sequential Variational Autoencoder for Collaborative Filtering per WU Mei-lin, HUANG Jia-jin, QIN Jin

Matèries: “…variational autoencoder|deep learning|sequence modeling|disentangled learning|collaborative filtering…”
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Generative Oversampling with a Contrastive Variational Autoencoder per Dai, Wangzhi, Ng, Kenney, Severson, Kristen, Huang, Wei, Anderson, Fred, Stultz, Collin

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Generative Oversampling with a Contrastive Variational Autoencoder per Dai, Wangzhi, Ng, Kenney, Severson, Kristen A, Huang, Wei, Anderson, Fred, Stultz, Collin M

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Variational Autoencoders for Discovering Influential Latent Factors per Hu, William

“…In this vein, variational autoencoders (VAEs) and their variants are one technique of generative modeling (and therefore representation learning) using variational inference under the assumption that the underlying data distribution is composed of a few latent random variables. …”
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Improving representation learning through variational autoencoding per Lin, S

“…</p> <p>In this thesis, we focus on representations derived through a specific type of generative model, i.e. variational autoencoders (VAEs). VAEs have several desirable properties. …”

Learning hard quantum distributions with variational autoencoders per Rocchetto, A, Grant, E, Strelchuk, S, Carleo, G, Severini, S

Variational autoencoders for supervision, calibration and multimodal learning per Joy, TW

“…An effective way to learn representations is through a Variational Autoencoder (VAE), which performs variational inference on the latent variables of the observable input. …”

Multi-Channel Multi-Scale Convolution Attention Variational Autoencoder (MCA-VAE): An Interpretable Anomaly Detection Algorithm Based on Variational Autoencoder per Jingwen Liu, Yuchen Huang, Dizhi Wu, Yuchen Yang, Yanru Chen, Liangyin Chen, Yuanyuan Zhang

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An Information-Theoretic Perspective on Proper Quaternion Variational Autoencoders per Eleonora Grassucci, Danilo Comminiello, Aurelio Uncini

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Variational Autoencoder-Based Framework for Retail Sales Prediction per Fuyu Li, Lei Wang, Bo Jin

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Semi-Supervised Variational Autoencoders for Out-of-Distribution Generation per Frantzeska Lavda, Alexandros Kalousis

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Variational autoencoders learn transferrable representations of metabolomics data per Daniel P. Gomari, Annalise Schweickart, Leandro Cerchietti, Elisabeth Paietta, Hugo Fernandez, Hassen Al-Amin, Karsten Suhre, Jan Krumsiek

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Network Embedding via Community Based Variational Autoencoder per Wei Shi, Ling Huang, Chang-Dong Wang, Juan-Hui Li, Yong Tang, Chengzhou Fu

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Classical Music Prediction and Composition by Means of Variational Autoencoders per Daniel Rivero, Iván Ramírez-Morales, Enrique Fernandez-Blanco, Norberto Ezquerra, Alejandro Pazos

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Detection of Anomalous Grapevine Berries Using Variational Autoencoders per Miro Miranda, Laura Zabawa, Anna Kicherer, Laurenz Strothmann, Uwe Rascher, Ribana Roscher, Ribana Roscher

“…Specifically, we train a fully convolutional variational autoencoder with a feature perceptual loss on images with healthy berries only and consider image areas with deviations from this model as damaged berries. …”
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