{"total":20,"rows":[{"id":"2306.02235","title":"Learning Linear Causal Representations from Interventions under General Nonlinear Mixing","created":"2023-06-04","cited_by_count":92,"area":null},{"id":"2206.07751","title":"On the Identifiability of Nonlinear ICA: Sparsity and Beyond","created":"2022-06-15","cited_by_count":85,"area":null},{"id":"2311.04056","title":"Multi-View Causal Representation Learning with Partial Observability","created":"2023-11-07","cited_by_count":74,"area":null},{"id":"2206.10044","title":"Identifiability of deep generative models without auxiliary information","created":"2022-06-20","cited_by_count":70,"area":null},{"id":"2302.02672","title":"Identifiability of latent-variable and structural-equation models: from linear to nonlinear","created":"2023-02-06","cited_by_count":58,"area":null},{"id":"2304.07939","title":"Leveraging sparse and shared feature activations for disentangled representation learning","created":"2023-04-17","cited_by_count":32,"area":null},{"id":"2310.15709","title":"Causal Representation Learning Made Identifiable by Grouping of Observational Variables","created":"2023-10-24","cited_by_count":32,"area":null},{"id":"2402.09236","title":"Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models","created":"2024-02-14","cited_by_count":29,"area":null},{"id":"2308.02652","title":"A Review of Change of Variable Formulas for Generative Modeling","created":"2023-08-04","cited_by_count":13,"area":null},{"id":"2301.12351","title":"Emerging Synergies in Causality and Deep Generative Models: A Survey","created":"2023-01-29","cited_by_count":10,"area":null},{"id":"2203.11132","title":"Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare","created":"2022-03-21","cited_by_count":9,"area":null},{"id":"2406.07141","title":"Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention","created":"2024-06-11","cited_by_count":9,"area":null},{"id":"2503.00639","title":"Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning","created":"2025-03-01","cited_by_count":4,"area":null},{"id":"2603.23547","title":"PDGMM-VAE: A Variational Autoencoder with Adaptive Per-Dimension Gaussian Mixture Model Priors for Nonlinear ICA","created":"2026-03-20","cited_by_count":4,"area":null},{"id":"2307.05704","title":"A Causal Ordering Prior for Unsupervised Representation Learning","created":"2023-07-11","cited_by_count":3,"area":null},{"id":"2306.14861","title":"Leveraging Task Structures for Improved Identifiability in Neural Network Representations","created":"2023-06-26","cited_by_count":1,"area":null},{"id":"2507.04754","title":"Intervening to Learn and Compose Causally Disentangled Representations","created":"2025-07-07","cited_by_count":1,"area":null},{"id":"2604.13218","title":"Identifiability of Potentially Degenerate Gaussian Mixture Models With Piecewise Affine Mixing","created":"2026-04-14","cited_by_count":1,"area":null},{"id":"2405.18655","title":"CAVACHON: a hierarchical variational autoencoder to integrate multi-modal single-cell data","created":"2024-05-28","cited_by_count":0,"area":null},{"id":"2506.07806","title":"Identifiable Object Representations under Spatial Ambiguities","created":"2025-06-09","cited_by_count":0,"area":null}],"next_cursor":null}