SI-NP applies self-normalized importance sampling within an EM surrogate objective, yielding consistent log-likelihood gains over NP, CNP, and ML-NP on regression and image completion benchmarks.
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Bridge the Inference Gaps of Neural Processes via Expectation Maximization
SI-NP applies self-normalized importance sampling within an EM surrogate objective, yielding consistent log-likelihood gains over NP, CNP, and ML-NP on regression and image completion benchmarks.