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Showing 1 to 5 of 5 for “"normalizing flow"”.
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Generalized flow-based variational autoencoder networks for anomaly detection in multivariate time series
… approximations are explored through the use of normalizing flows. By applying normalizing flow transforms to the latent variables of a variational autoencoder, the true latent distribution can be more richly modeled and learned, thus enabling better metrics for anomaly detection. This thesis …
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Non-Gaussian Factor Graph Inference for Robotic Navigation
… With this conditional sampling framework, we use normalizing flows to learn local conditional distributions on cliques of the Bayes tree. The normalizing flows exploit the expressive power of neural networks, and train a coupling function that connects a low-dimensional non-Gaussian distribution …
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Advances in Symbolic Regression: From Generalized Formulation to Density Estimation and Inverse Problem
… its versatility, ISR also serves as a symbolic normalizing flow for density estimation tasks. Additionally, we showcase its applicability in solving inverse problems, including a benchmark inverse kinematics problem, and notably, a geoacoustic inversion problem in oceanography aimed at inferring …
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Modeling Structured Data with Invertible Generative Models
… and have been applied to many problems. Normalizing flows are a novel class of deep generative models that allow efficient exact likelihood calculation, exact latent variable inference and sampling. They are constructed using functions whose inverse and Jacobian determinant can be …
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Improving Photometric Camera Accuracy and Image Quality in High Dynamic Range Imaging
Cameras have long grappled with the challenge of capturing the vast range of light intensities present in the real world and reproducing them on a medium with much lower dynamic range. This challenge persisted from the era of print photography and continued with the adoption of standard dynamic …