Kernel Mean Embedding for Distributional Input

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The use of Kernel Mean Embeddings (KMEs) to represent the input probability distribution in the D2D framework.

Why It Matters

It provides the specific mechanism for encoding the necessary probabilistic input state into the neural network module.

Evidence

using kernel mean embeddings to represent input distributions

Related Papers

• openalex-2603.25370-a-distribution-to-distribution-neural-probabilistic-forecast