Introduces CSDI as a structural condition for identifiability of content and style in nonlinear generative mixtures, operationalized via blockwise Jacobian orthogonality and a stochastic regularizer.
Transactions on Machine Learning Research , issn=
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.
citing papers explorer
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Content-Style Identification via Differential Independence
Introduces CSDI as a structural condition for identifiability of content and style in nonlinear generative mixtures, operationalized via blockwise Jacobian orthogonality and a stochastic regularizer.
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What Makes a Representation Good for Single-Cell Perturbation Prediction?
PerturbedVAE disentangles perturbation-specific signals from invariant gene expression structure to recover causal representations and improve out-of-distribution prediction in single-cell perturbation modeling.