{"total":34,"rows":[{"id":"2306.00542","title":"Nonparametric Identifiability of Causal Representations from Unknown Interventions","created":"2023-06-01","cited_by_count":106,"area":null},{"id":"2307.02598","title":"Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation","created":"2023-07-05","cited_by_count":58,"area":null},{"id":"2305.18378","title":"Disentanglement via Latent Quantization","created":"2023-05-28","cited_by_count":40,"area":null},{"id":"2410.21869","title":"Cross-Entropy Is All You Need To Invert the Data Generating Process","created":"2024-10-29","cited_by_count":31,"area":null},{"id":"2302.02865","title":"Probabilistic Contrastive Learning Recovers the Correct Aleatoric Uncertainty of Ambiguous Inputs","created":"2023-02-06","cited_by_count":29,"area":null},{"id":"2408.14471","title":"A Practitioner's Guide to Continual Multimodal Pretraining","created":"2024-08-26","cited_by_count":23,"area":null},{"id":"2310.17653","title":"Fantastic Gains and Where to Find Them: On the Existence and Prospect of General Knowledge Transfer between Any Pretrained Model","created":"2023-10-26","cited_by_count":19,"area":null},{"id":"2405.06394","title":"Memory Mosaics","created":"2024-05-10","cited_by_count":16,"area":null},{"id":"2406.13371","title":"Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment","created":"2024-06-19","cited_by_count":12,"area":null},{"id":"2506.16506","title":"Subspace-Boosted Model Merging","created":"2025-06-19","cited_by_count":9,"area":null},{"id":"2402.19186","title":"Disentangling representations of retinal images with generative models","created":"2024-02-29","cited_by_count":8,"area":null},{"id":"2503.15355","title":"Robustness of Nonlinear Representation Learning","created":"2025-03-19","cited_by_count":7,"area":null},{"id":"2405.01531","title":"Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models","created":"2024-05-02","cited_by_count":6,"area":null},{"id":"2403.09830","title":"Towards the Reusability and Compositionality of Causal Representations","created":"2024-03-14","cited_by_count":5,"area":null},{"id":"2503.15699","title":"Representational Similarity via Interpretable Visual Concepts","created":"2025-03-19","cited_by_count":5,"area":null},{"id":"2507.03285","title":"Memory Mosaics at scale","created":"2025-07-04","cited_by_count":5,"area":null},{"id":"2308.12696","title":"Disentanglement Learning via Topology","created":"2023-08-24","cited_by_count":4,"area":null},{"id":"2410.06232","title":"Range, not Independence, Drives Modularity in Biologically Inspired Representations","created":"2024-10-08","cited_by_count":4,"area":null},{"id":"2411.00588","title":"$\\alpha$-TCVAE: On the relationship between Disentanglement and Diversity","created":"2024-11-01","cited_by_count":4,"area":null},{"id":"2306.16334","title":"On the Identifiability of Quantized Factors","created":"2023-06-28","cited_by_count":3,"area":null},{"id":"2407.07829","title":"Disentangled Representation Learning with the Gromov-Monge Gap","created":"2024-07-10","cited_by_count":3,"area":null},{"id":"2304.02335","title":"Correcting Flaws in Common Disentanglement Metrics","created":"2023-04-05","cited_by_count":2,"area":null},{"id":"2305.11512","title":"Enriching Disentanglement: From Logical Definitions to Quantitative Metrics","created":"2023-05-19","cited_by_count":2,"area":null},{"id":"2602.16161","title":"Emotion Collider: Dual Hyperbolic Mirror Manifolds for Sentiment Recovery via Anti Emotion Reflection","created":"2026-02-18","cited_by_count":2,"area":null},{"id":"2510.21402","title":"Disentangled Representation Learning via Modular Compositional Bias","created":"2025-10-24","cited_by_count":1,"area":null},{"id":"2303.00128","title":"Representation Disentaglement via Regularization by Causal Identification","created":"2023-02-28","cited_by_count":0,"area":null},{"id":"2306.09005","title":"Modularity Trumps Invariance for Compositional Robustness","created":"2023-06-15","cited_by_count":0,"area":null},{"id":"2310.15999","title":"Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships","created":"2023-10-24","cited_by_count":0,"area":null},{"id":"2408.07016","title":"Rethinking Disentanglement under Dependent Factors of Variation","created":"2024-08-13","cited_by_count":0,"area":null},{"id":"2510.16289","title":"Disentangling Hyperedges through the Lens of Category Theory","created":"2025-10-18","cited_by_count":0,"area":null},{"id":"2511.20927","title":"Operationalizing Quantized Disentanglement","created":"2025-11-25","cited_by_count":0,"area":null},{"id":"2607.17264","title":"Coordinated Disentanglement with Iterative Mode Discovery Under Hidden Correlations","created":"2026-07-19","cited_by_count":0,"area":null},{"id":"2608.16245","title":"The Trade-off Between Covariate Dependence and Latent Structure in Representation Learning","created":"2026-08-17","cited_by_count":0,"area":null},{"id":"2608.24762","title":"Beyond Uniform Local Isometry and Topology: FactoMap for Disentangled Representations","created":"2026-08-25","cited_by_count":0,"area":null}],"next_cursor":null}