SimPEL uses low-fidelity simulators plus a Gaussian-process gap as a functional prior for Bayesian neural networks, improving data efficiency in dynamics learning and model-based RL.
In: International Conference on Learning Representations (2019)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Simulation Priors for Data-Efficient Deep Learning
SimPEL uses low-fidelity simulators plus a Gaussian-process gap as a functional prior for Bayesian neural networks, improving data efficiency in dynamics learning and model-based RL.