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Recommendations on test datasets for evaluating AI solutions in pathology
The recommendations are intended to help AI developers demonstrate the utility of their products and to help regulatory agencies and end users verify reported performance measures.
Planning with Noisy Probabilistic Relational Rules
They are compact and generalize over world instantiations.
Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress
Formal exploration approaches in model-based reinforcement learning estimate the accuracy of the currently learned model without consideration of the empirical prediction error.
Model-based Reinforcement Learning
reinforcement-learning
+2
