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Arxiv
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Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model
- Uncertainty quantification is important in reinforcement learning and deep learning.
- Contextual similarity distillation is proposed as a method to estimate ensemble variance with a single model.
- The method is based on predictable learning dynamics of wide neural networks.
- The single-model method performs competitively and serves as a reliable signal for efficient exploration.
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