{"data":{"id":"fc749ba1-6997-4a2c-ab0b-018ca395e383","title":"Monte Carlo Marginalization: A Differentiable Method to Learn High-Dimensional Distributions","summary":"Researchers propose Monte Carlo marginalization (MCMarg), a differentiable method that fits a Gaussian mixture model to an intractable high-dimensional distribution by minimizing KL divergence, with kernel density estimation keeping the optimization differentiable. The method reports FID scores of 22 on MNIST without a neural network and 2.69 on CIFAR-10, and it improves pretrained VAEs by about 10 FID points when replacing their standard priors.","solution":"N/A -- no mitigation discussed in source.","labels":["research"],"sourceUrl":"http://ieeexplore.ieee.org/document/11494096","publishedAt":"2026-04-23T13:16:32.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-04-23T13:16:32.000Z","fixReleasedAt":null,"daysToFix":null,"fixCheckedAt":null,"capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":null,"llmSpecific":false,"classifierConfidence":0.6,"researchCategory":"peer_reviewed","atlasIds":null}}