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Showing 1 to 4 of 4 for “"Generative Networks"”.
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Deep generative models via explicit Wasserstein minimization
This thesis provides a procedure to fit generative networks to target distributions, with the goal of a small Wasserstein distance (or other optimal transport costs). The approach is based on two principles: (a) if the source randomness of the network is a continuous distribution (the …
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Learning distributions with Particle Mirror Descent
… Bayesian probabilistic inference and deep neural networks, deep generative networks have shown remarkable success in various kinds of generative tasks. However, such models usually make an assumption that posterior distribution can be simply characterized as a Gaussian distribution, which is not …
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Deep learning methods to study structurally heterogeneous macromolecules in vitro and in situ
… by the cryoDRGN and tomoDRGN Deep Reconstructing Generative Networks.
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Robust and high performance machine learning for next generation wireless networks
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms