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meta-learning [2018/11/12 22:18]
meta-learning [2018/12/23 04:09]
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 passes through inference networks, amortizing the cost of inference and relieving passes through inference networks, amortizing the cost of inference and relieving
 the need for second derivatives during training. the need for second derivatives during training.
 +https://​arxiv.org/​pdf/​1810.06784.pdf PROMP: PROXIMAL META-POLICY SEARCH
 +This paper provides
 +a theoretical analysis of credit assignment in gradient-based Meta-RL. Building
 +on the gained insights we develop a novel meta-learning algorithm that overcomes
 +both the issue of poor credit assignment and previous difficulties in estimating
 +meta-policy gradients. By controlling the statistical distance of both
 +pre-adaptation and adapted policies during meta-policy search, the proposed algorithm
 +endows efficient and stable meta-learning. Our approach leads to superior
 +pre-adaptation policy behavior and consistently outperforms previous Meta-RL algorithms
 +in sample-efficiency,​ wall-clock time, and asymptotic performance. Our
 +code is available at github.com/​jonasrothfuss/​promp
 +https://​pdfs.semanticscholar.org/​0b00/​3bb28f25627f715b0fd53b443fabfcf5a817.pdf?​_ga=2.110922695.354576531.1543161615-2107301068.1536926320 Meta-Learning with Latent Embedding Optimization
 +The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks
 +https://​arxiv.org/​pdf/​1810.03642.pdf CAML: FAST CONTEXT ADAPTATION VIA META-LEARNING
 +https://​arxiv.org/​pdf/​1611.03537.pdf Linear predictors for nonlinear dynamical
 +systems: Koopman operator meets model
 +predictive control
 +http://​metalearning.ml/​2018/​slides/​meta_learning_2018_Levine.pdf What’s Wrong with Meta-Learning