Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision
Previous revision
meta-learning [2018/10/10 11:54]
admin
meta-learning [2018/12/23 04:11] (current)
admin
Line 426: Line 426:
  
 In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field. In this chapter, we provide an overview of the state of the art in this fascinating and continuously evolving field.
 +
 +https://​arxiv.org/​pdf/​1810.03642v1.pdf CAML: Fast Context Adaptation via Meta-Learning
 +
 +
 +CAML: Fast Context Adaptation via Meta-Learning
 +Luisa M Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, Shimon Whiteson
 +(Submitted on 8 Oct 2018 (this version), latest version 12 Oct 2018 (v2))
 +We propose CAML, a meta-learning method for fast adaptation that partitions the model parameters into two parts: context parameters that serve as additional input to the model and are adapted on individual tasks, and shared parameters that are meta-trained and shared across tasks.
 +
 +https://​arxiv.org/​pdf/​1810.08178.pdf Gradient Agreement as an Optimization Objective for
 +Meta-Learning
 +
 +Our approach is based on pushing the parameters of the
 +model to a direction in which tasks have more agreement upon. If the gradients
 +of a task agree with the parameters update vector, then their inner product will be
 +a large positive value. As a result, given a batch of tasks to be optimized for, we
 +associate a positive (negative) weight to the loss function of a task, if the inner
 +product between its gradients and the average of the gradients of all tasks in the
 +batch is a positive (negative) value. ​
 +
 +https://​openreview.net/​pdf?​id=HkxStoC5F7 META-LEARNING PROBABILISTIC INFERENCE FOR
 +PREDICTION
 +
 + 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
 +
 +Meta-learning,​ or learning to learn, offers an appealing framework for training deep neural networks to adapt quickly and efficiently to new tasks. Indeed, the framework of meta-learning holds the promise of resolving the long-standing challenge of sample complexity in deep learning: by learning to learn efficiently,​ deep models can be meta-trained to adapt quickly to classify new image classes from a couple of examples, or learn new skills with reinforcement learning from just a few trials.
 +
 +However, although the framework of meta-learning and few-shot learning is exceedingly appealing, it carries with it a number of major challenges. First, designing neural network models for meta-learning is quite difficult, since meta-learning models must be able to ingest entire datasets to adapt effectively. I will discuss how this challenge can be addressed by describing a model-agnostic meta-learning algorithm: a meta-learning algorithm that can use any model architecture,​ training that architecture to adapt efficiently via simple finetuning.
 +
 +The second challenge is that meta-learning trades off the challenge of algorithm design (by learning the algorithm) for the challenge of task design: the performance of meta-learning algorithms depends critically on the ability of the user to manually design large sets of diverse meta-training tasks. In practice, this often ends up being an enormous barrier to widespread adoption of meta-learning methods. I will describe our recent work on unsupervised meta-learning,​ where tasks are proposed automatically from unlabeled data, and discuss how unsupervised meta-learning can exceed the performance of standard unsupervised learning methods while removing the manual task design requirement inherent in standard meta-learning methods.