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Model selection is treated as a standard performance boosting step in many machine learning applications.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
R.J. Williams · 1992
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Online choice of active learning algorithms
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Ralf: A reinforced active learning formulation for object class recognition
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Collaborative gaussian processes for preference learning
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Fast dropout training
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Weight uncertainty in neural network
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Probabilistic backpropagation for scalable learning of bayesian neural networks
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Trust region policy optimization
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Can active learning experience be transferred?
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Proximal policy optimization algorithms
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Cost-effective active learning for deep image classification
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Learning to select computations
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Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning
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Meta-learning transferable active learning policies by deep reinforcement learning
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Active learning for convolutional neural networks: Acore-set approach
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Sampling-free variational inference for bayesian neural networks by variance backpropagation
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