Latent Matters : Amortised Variational Inference With Constrained Optimisation and Learnable Priors
- Alternative title
-
Die Bedeutung latenter Repräsentationen
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
-
Englisch
- Notes
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München, Technische Universität München, Dissertation, 2021
- Keyword
- Event
-
Veröffentlichung
- (where)
-
München
- (who)
-
Universitätsbibliothek der TU München
- (when)
-
2021
- Creator
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Klushyn, Alexej
- Contributor
- URN
-
urn:nbn:de:bvb:91-diss-20211202-1614761-1-8
- Rights
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
25.03.0014, 1:51 PM CET
Data provider
This object is provided by:
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Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Klushyn, Alexej
- Bungartz, Hans-Joachim
- Groh, Georg
- Universitätsbibliothek der TU München
Time of origin
- 2021
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Analysis and applications of variational sensitivity information in structural optimisation
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Projection methods in constrained optimisation and applications to optimal policy decisions
Adaptive and tractable Bayesian context inference for resource constrained devices
Several path-following methods for a class of gradient constrained variational inequalities
Metric Gaussian variational inference
Variational inference for composite Gaussian process models
Efficient Gradient-Free Variational Inference using Policy Search
Trust-Region Variational Inference with Gaussian Mixture Models
Moment-Based Variational Inference for Stochastic Differential Equations
Bathymetry reconstruction from experimental data using PDE-constrained optimisation
Analysis and applications of variational sensitivity information in structural optimisation
Analysis and applications of variational sensitivity information in structural optimisation
Scalable Inference in Latent Gaussian Process Models
Projection methods in constrained optimisation and applications to optimal policy decisions
Adaptive and tractable Bayesian context inference for resource constrained devices