Search Results - extrapolation adversarial variational autoencoder

Study on data augmentation with physics-informed generative adversarial networks and the extrapolation performance of COP prediction for chillers by Wang, Zhengyang, Chen, Jun, Guo, Kexin, Xu, Bo, Chen, Zhenqian

“…•Vendi Score and Mahalanobis distance quantify data diversity and extrapolation risk.•Physics-informed samples enhance physical consistency and statistical reliability…”
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Supervising Deep Learning Models with Autoencoder Based Approaches by Kühne, Joana

“… durchgeführten Vorhersagen zu überwachen. Da künstliche neuronale Netze datenbasierte Modelle sind, und somit nicht auf universellen physikalischen oder chemischen Prinzipien beruhen, fehlt es ihnen oft an Extrapolations…”
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Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters by Martínez-Palomera, Jorge, Bloom, Joshua S., Abrahams, Ellianna S.

“… Unlike fully theory-driven models, these approaches do not typically allow principled interpolation nor extrapolation…”
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Improving the Performance of Batch-Constrained Reinforcement Learning in Continuous Action Domains via Generative Adversarial Networks by Saglam, Baturay, Dalmaz, Onat, Gonc, Kaan, Kozat, Suleyman S.

“… However, due to conditional Variational Autoencoders (VAE) used in the data generation module, the BCQ algorithm optimizes a lower variational bound and hence, it is not generalizable to environments with large state and action spaces…”
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One-shot generative distribution matching for augmented RF-based UAV identification by Kazemi, Amir, Basiri, Salar, Kindratenko, Volodymyr, Salapaka, Srinivasa

“…) and variational autoencoders (VAEs). The paper provides a theoretical guarantee for the effectiveness…”
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NEURAL ORDINARY DIFFERENTIAL EQUATIONS FOR TIME SERIES RECONSTRUCTION by Androsov, D. V.

“… of unevenly distributed samples. Objective. The goal of the following research is the synthesis of a deep neural network that is able to solve input signal reconstruction and time series extrapolation task. Method…”
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Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters by Martínez-Palomera, Jorge, Bloom, Joshua S, Abrahams, Ellianna S

“… Unlike fully theory-driven models, these approaches do not typically allow principled interpolation nor extrapolation…”
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Study of Deep Generative Models for Inorganic Chemical Compositions by Sawada, Yoshihide, Morikawa, Koji, Fujii, Mikiya

“…Generative models based on generative adversarial networks (GANs) and variational autoencoders (VAEs…”
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