Diverse dictionary learning identifies intersections, complements, and dependency structures of latent variables from data X = g(Z) up to indeterminacies, and full identifiability when structural diversity is sufficient.
arXiv preprint arXiv:2001.04872 , year=
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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.
Derives optimality constraints for nonnegative joint dictionary learning that explain observed SAE behaviors such as feature splitting, absorption, and dense antipodal features.
Proposes CITE-VAE, a latent dynamical causal VAE with identifiability analysis for single-cell perturbation prediction, claiming better OOD generalization on CRISPR data.
citing papers explorer
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Diverse Dictionary Learning
Diverse dictionary learning identifies intersections, complements, and dependency structures of latent variables from data X = g(Z) up to indeterminacies, and full identifiability when structural diversity is sufficient.
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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.
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How Optimality Structures Sparse Dictionaries: A Theory for Understanding SAE Representations
Derives optimality constraints for nonnegative joint dictionary learning that explain observed SAE behaviors such as feature splitting, absorption, and dense antipodal features.
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Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction
Proposes CITE-VAE, a latent dynamical causal VAE with identifiability analysis for single-cell perturbation prediction, claiming better OOD generalization on CRISPR data.
- Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability