Suchergebnisse - conditioning variational (autoencoder（cvae） OR autoencoder（cae）)*

Development of a surrogate model to improve the efficiency of groundwater level fluctuation pattern-based hydrologic properties evaluation von Jeong, Jiho, Jeong, Jina

“… •A method to evaluate hydraulic properties using the groundwater level (GL).•Extraction of informative features of GL conditioning with a precipitation pattern …”
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Deep conditional generative model for personalization of 12-lead electrocardiograms and cardiovascular risk prediction von Sang, Yuling, Banerjee, Abhirup, Beetz, Marcel, Grau, Vicente

“… We propose a conditional Variational Autoencoder (cVAE) framework to generate realistic, subject-specific 12-lead ECGs …”
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Generating multiperspective process traces using conditional variational autoencoders von Graziosi, Riccardo, Ronzani, Massimiliano, Buliga, Andrei, Di Francescomarino, Chiara, Folino, Francesco, Ghidini, Chiara, Meneghello, Francesca, Pontieri, Luigi

“… In recent years, trace generation has emerged as a significant challenge within the Process Mining community. Deep Learning (DL) models have demonstrated …”
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Framing the Sequence: Genre-Aligned Photo Curation via Shot-Scale Embedding von Park, Youngsup, Lim, Yangmi, Kang, Dongwann

“… ) a conditional variational autoencoder (cVAE) for embedding temporal shot rhythms conditioned on genre, and (3 …”
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조건부 생성모델을 이용한 강수 패턴에 따른 지하수위 생성 및 이의 활용에 관한 연구 von 정지호(Jiho Jeong), 정진아(Jina Jeong), 이병선(Byung Sun Lee), 송성호(Sung-Ho Song)

“… of the regression model. To solve the above problem, a process for generating the GWL fluctuation pattern for conditioning the precipitation is proposed …”
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Application of conditional generative model for sonic log estimation considering measurement uncertainty von Jeong, Jina, Park, Eungyu, Emelyanova, Irina, Pervukhina, Marina, Esteban, Lionel, Yun, Seong-Taek

“… The developed method is based on the conditional variational autoencoder (CVAE) and effectively considers uncertainty associated with the variability of the measured data …”
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Trust-Act: Integrating Trust in Imitation Learning von Lingg, Nico, Demiris, Yiannis

“… In this paper, we present Trust-Act, a framework that integrates trust labels into a conditional variational autoencoder (CVAE …”
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Fusion U and V: Efficient MRI Sampling via the Fusion of U-net and Conditional Variational Autoencoder von Pershina, Khristina, Huang, Ching Chun

“… Our paper introduces a novel approach, Fusion UandV, which employs a Conditional Variational Autoencoder (cVAE …”
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The Applicability of Conditional Generative Model Generating Groundwater Level Fluctuation Corresponding to Precipitation Pattern von Jeong, Jiho, Jeong, Jina, Lee, Byung Sun, Song, Sung-Ho

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Style Conditioned Recommendations von Iqbal, Murium, Aryafar, Kamelia, Anderton, Timothy

“… We use Conditional Variational Autoencoder (CVAE) architecture, where both the encoder and decoder are conditioned on a user profile learned from item content data …”
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SurpriseNet: Melody Harmonization Conditioning on User-controlled Surprise Contours von Yi-Wei, Chen, Hung-Shin, Lee, Yen-Hsing, Chen, Wang, Hsin-Min

“… Based on this, we propose a user-controllable framework that uses a conditional variational autoencoder (CVAE …”
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Contextually Plausible and Diverse 3D Human Motion Prediction von Aliakbarian, Sadegh, Fatemeh Sadat Saleh, Petersson, Lars, Gould, Stephen, Salzmann, Mathieu

“… In this context, a popular approach consists of using a Conditional Variational Autoencoder (CVAE). However, existing approaches that do so either fail to capture …”
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Balancing Exploration and Exploitation: Disentangled \(\beta\)-CVAE in De Novo Drug Design von Guang Jun Nicholas Ang, De Tao Irwin Chin, Shen, Bingquan

“… In this respect, deep generative conditional variational autoencoder (CVAE) models are a powerful approach for generating novel molecules with desired drug-like properties …”
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Class-Conditioned Variational Autoencoder with Evolutionary Optimization for the Virtual Data Generation Challenge von Tachioka, Yuuki

“… To address this issue, we propose a novel data augmentation framework using conditional variational autoencoders (CVAE …”
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Learning conditional variational autoencoders with missing covariates von Ramchandran, Siddharth, Tikhonov, Gleb, Lönnroth, Otto, Tiikkainen, Pekka, Lähdesmäki, Harri

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Physical discovery in representation learning via conditioning on prior knowledge: applications for ferroelectric domain dynamics von Liu, Yongtao, Huey, Bryan D, Ziatdinov, Maxim A, Kalinin, Sergei V

“… Here, we explore an approach based on conditioning the data on the known (continuous) physical parameters, and systematically compare it with the …”
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Learning Conditional Variational Autoencoders with Missing Covariates von Ramchandran, Siddharth, Tikhonov, Gleb, Lönnroth, Otto, Tiikkainen, Pekka, Lähdesmäki, Harri

“… Conditional variational autoencoders (CVAEs) are versatile deep generative models that extend the standard VAE framework by conditioning the generative model with auxiliary covariates …”
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Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems von Sorouifar, Farshud, Paulson, Joel A., Wang, Ye, Quirynen, Rien, Laughman, Christopher R., Chakrabarty, Ankush

“… Generative models, such as conditional variational autoencoders (CVAEs), provide an expressive and automated approach for learning distributions from data …”
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Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction von Schmerling, Edward, Leung, Karen, Wolf Vollprecht, Pavone, Marco

“… This paper presents a method for constructing human-robot interaction policies in settings where multimodality, i.e., the possibility of multiple highly …”
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