Results 11 to 20 of about 4,772,239 (326)
The Variational InfoMax AutoEncoder [PDF]
2020 International Joint Conference on Neural Networks (IJCNN), 2020 The Variational AutoEncoder (VAE) learns simultaneously an inference and a generative model, but only one of these models can be learned at optimum, this behaviour is associated to the ELBO learning objective, that is optimised by a non-informative generator.
Crescimanna, Vincenzo, Graham, Bruce
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Interpretable Variational Graph Autoencoder with Noninformative Prior
Future Internet, 2021 Variational graph autoencoder, which can encode structural information and attribute information in the graph into low-dimensional representations, has become a powerful method for studying graph-structured data.
Lili Sun, Xueyan Liu, Min Zhao, Bo Yang
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eVAE: Evolutionary Variational Autoencoder [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2023 Variational autoencoders (VAEs) are challenged by the imbalance between representation inference and task fitting caused by surrogate loss. To address this issue, existing methods adjust their balance by directly tuning their coefficients. However, these
Zhangkai Wu, Long-Bing Cao, Lei Qi
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Importance weighted variational graph autoencoder
Complex & Intelligent SystemsVariational Graph Autoencoder (VGAE) is a widely explored model for learning the distribution of graph data. Currently, the approximate posterior distribution in VGAE-based methods is overly restrictive, leading to a significant gap between the ...
Yuhao Tao +3 more
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Joint Coding-Modulation for Digital Semantic Communications via Variational Autoencoder [PDF]
IEEE Transactions on Communications, 2023 Semantic communications have emerged as a new paradigm for improving communication efficiency by transmitting the semantic information of a source message that is most relevant to a desired task at the receiver. Most existing approaches typically utilize
Yu-Fei Bo +3 more
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Causal Recurrent Variational Autoencoder for Medical Time Series Generation [PDF]
AAAI Conference on Artificial Intelligence, 2023 We propose causal recurrent variational autoencoder (CR-VAE), a novel generative model that is able to learn a Granger causal graph from a multivariate time series x and incorporates the underlying causal mechanism into its data generation process ...
Hong-Ming Li, Shu-Jian Yu, J. PrÃncipe
semanticscholar +1 more source
A Predictor-Corrector Methods for Mixed Inverse Variational Inequalities [PDF]
, 2009 In this paper, a class of mixed inverse variational inequalities is introduced and studied. We prove the existence of the solution of the auxiliary problem for mixed inverse variational inequalities, suggest a predictor-corrector method for solving the ...
Shi, Chaofeng
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Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Manifold-valued data naturally arises in medical imaging. In cognitive neuroscience, for instance, brain connectomes base the analysis of coactivation patterns between different brain regions on the analysis of the correlations of their functional Magnetic Resonance Imaging (fMRI) time series - an object thus constrained by construction to belong to ...
Nina Miolane, Susan P. Holmes
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Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimisation [PDF]
, 2022 In this work, we provide further development of the junction tree variational autoencoder (JT VAE) architecture in terms of implementation and application of the internal feature space of the model.
Marian, Dryzhakov +3 more
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Variational Autoencoders Without the Variation
CoRR, 2022 11 pages, 7 figures, 3 ...
Gregory A. Daly +2 more
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