Information Bottleneck
ReInfoSelect
Introduced by Zhang et al. in Selective Weak Supervision for Neural Information RetrievalReInfoSelect is a reinforcement weak supervision selection method for information retrieval. It learns to select anchor-document pairs that best weakly supervise the neural ranker (action), using the ranking performance on a handful of relevance labels as the reward. Iteratively, for a batch of anchor-document pairs, ReInfoSelect back propagates the gradients through the neural ranker, gathers its NDCG reward, and optimizes the data selection network using policy gradients, until the neural ranker's performance peaks on target relevance metrics (convergence).
Source: Selective Weak Supervision for Neural Information Retrieval
Papers
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Usage Over Time
Components
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| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
