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Variational Inference research benchmarks – Page 2 · SOTA2 Research

Variational Inference

Benchmarks

Dataset NameSOTA MethodSort

Dataset NameSOTA methodMetricTrendResultsLast Updated

Variance Collapse d=200

0.11DAMV

4

Jun 25, 2026

Variance Collapse (d=50)

0.16DAMV

4

Jun 25, 2026

1.8RKL

4

Apr 21, 2026

2.2RKL

4

Apr 21, 2026

2.9RKL

4

Apr 21, 2026

1.1RKL

4

Apr 21, 2026

1.1RKL

4

Apr 21, 2026

1.8RKL

4

Apr 21, 2026

2.7RKL

3

Apr 21, 2026

2.3RKL

3

Apr 21, 2026

Isotropic Student-t d=100

0.189Wasserstein Distance

3

Feb 26, 2026

Isotropic Logistic d=100

0.196Wasserstein Distance

3

Feb 26, 2026

Isotropic Laplace d=100

0.077Est. Wasserstein Distance

3

Feb 26, 2026

Isotropic Gaussian d=100

0Wasserstein Distance

3

Feb 26, 2026

Isotropic Student-t d=50

0.119Estimated Wasserstein Distance

3

Feb 26, 2026

Isotropic Logistic d=50

0.184Wasserstein Distance

3

Feb 26, 2026

Isotropic Laplace d=50

0.054Est. Wasserstein Distance

3

Feb 26, 2026

Isotropic Gaussian d=50

0Wasserstein Distance

3

Feb 26, 2026

Variational Inference Strongly convex assumptions

—

—Primary metric

0

Feb 26, 2026

Variational Inference Convex assumptions

—

—Primary metric

0

Feb 26, 2026

Variational Inference General assumptions

—

—Primary metric

0

Feb 26, 2026

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Student-t distribution Variational Inference Hollow MNIST Omniglot

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