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Sibling-aware replay improves learning efficiency over PER, although matched parameter sweeps show that tuning can narrow some gaps, and decompose PER into between-group allocation and conditional sibling selection, and derive fixed-buffer corrections that preserve current group-level priority mass.
Deep Policy Dynamic Programming is proposed, which aims to combine the strengths of learned neural heuristics with those of DP algorithms, and prioritizes and restricts the DP state space using a policy derived from a deep neural network, which is trained to predict edges from example solutions.
This work proposes a novel LAL method for classification that exploits symmetry and independence properties of the active learning problem with an Attentive Conditional Neural Process model and gives the model the ability to adapt to non-standard objectives.
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