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meta-learning [2018/10/19 20:54]
meta-learning [2018/12/23 04:09]
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 product between its gradients and the average of the gradients of all tasks in the product between its gradients and the average of the gradients of all tasks in the
 batch is a positive (negative) value. ​ batch is a positive (negative) value. ​
 +https://​openreview.net/​pdf?​id=HkxStoC5F7 META-LEARNING PROBABILISTIC INFERENCE FOR
 + 1) We develop ML-PIP, a general framework for Meta-Learning approximate
 +Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic
 +interpretations of meta-learning to cover a broad class of methods. 2) We
 +introduce VERSA, an instance of the framework employing a flexible and versatile
 +amortization network that takes few-shot learning datasets as inputs, with arbitrary
 +numbers of shots, and outputs a distribution over task-specific parameters in
 +a single forward pass. VERSA substitutes optimization at test time with forward
 +passes through inference networks, amortizing the cost of inference and relieving
 +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