Learning to inflate. A gradient ascent approach to random inflation
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Publication:5862073
DOI10.1088/1475-7516/2019/02/044OpenAlexW3105467760MaRDI QIDQ5862073
Publication date: 4 March 2022
Published in: Journal of Cosmology and Astroparticle Physics (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1810.05159
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Cites Work
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- On Gaussian random supergravity
- Observational consequences of a landscape
- Weakly supervised classification in high energy physics
- GANs for generating EFT models
- Axion decay constants away from the lamppost
- Deep learning in color: towards automated quark/gluon jet discrimination
- Systematics of axion inflation in Calabi-Yau hypersurfaces
- Inflation with a graceful exit in a random landscape
- Evolving neural networks with genetic algorithms to study the string landscape
- Machine learning in the string landscape
- Artificial neural network in cosmic landscape
- Inflation as an information bottleneck: a strategy for identifying universality classes and making robust predictions
- Topological data analysis for the string landscape
- Transplanckian axions!?
- Fencing in the swampland: quantum gravity constraints on large field inflation
- Weak gravity strongly constrains large-field axion inflation
- Initial conditions for slow-roll inflation in a random Gaussian landscape
- Seven lessons from manyfield inflation in random potentials
- Manyfield inflation in random potentials
- Persistent homology and non-Gaussianity
- Inflation in multi-field modified DBM potentials
- Inflation in random Gaussian landscapes
- Inflationary universe: A possible solution to the horizon and flatness problems
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