{"total":57,"rows":[{"id":"2109.03795","title":"Desiderata for Representation Learning: A Causal Perspective","created":"2021-09-08","cited_by_count":76,"area":null},{"id":"2311.17055","title":"No Representation Rules Them All in Category Discovery","created":"2023-11-28","cited_by_count":61,"area":null},{"id":"2206.06169","title":"Causal Representation Learning for Instantaneous and Temporal Effects in Interactive Systems","created":"2022-06-13","cited_by_count":51,"area":null},{"id":"2110.15255","title":"Self-Supervised Learning Disentangled Group Representation as Feature","created":"2021-10-28","cited_by_count":47,"area":null},{"id":"2211.14666","title":"Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning","created":"2022-11-26","cited_by_count":44,"area":null},{"id":"2310.04723","title":"Subspace Identification for Multi-Source Domain Adaptation","created":"2023-10-07","cited_by_count":37,"area":null},{"id":"2205.03962","title":"Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation","created":"2022-05-08","cited_by_count":32,"area":null},{"id":"2305.12218","title":"Text-Video Retrieval with Disentangled Conceptualization and Set-to-Set Alignment","created":"2023-05-20","cited_by_count":32,"area":null},{"id":"2106.15610","title":"An Image is Worth More Than a Thousand Words: Towards Disentanglement in the Wild","created":"2021-06-29","cited_by_count":30,"area":null},{"id":"2104.05622","title":"Where and What? Examining Interpretable Disentangled Representations","created":"2021-04-07","cited_by_count":27,"area":null},{"id":"2103.01638","title":"Learning disentangled representations via product manifold projection","created":"2021-03-02","cited_by_count":20,"area":null},{"id":"2207.09684","title":"On the Versatile Uses of Partial Distance Correlation in Deep Learning","created":"2022-07-20","cited_by_count":19,"area":null},{"id":"2210.05972","title":"Unsupervised Learning of Equivariant Structure from Sequences","created":"2022-10-12","cited_by_count":15,"area":null},{"id":"2311.04774","title":"Towards a Unified Framework of Contrastive Learning for Disentangled Representations","created":"2023-11-08","cited_by_count":13,"area":null},{"id":"2406.19298","title":"Compositional Image Decomposition with Diffusion Models","created":"2024-06-27","cited_by_count":12,"area":"diffusion"},{"id":"2210.01796","title":"Multi-objective Deep Data Generation with Correlated Property Control","created":"2022-10-01","cited_by_count":10,"area":null},{"id":"2311.04837","title":"Identifying Semantic Component for Robust Molecular Property Prediction","created":"2023-11-08","cited_by_count":10,"area":null},{"id":"2202.07260","title":"Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition","created":"2022-02-15","cited_by_count":9,"area":null},{"id":"2410.19923","title":"Language Agents Meet Causality -- Bridging LLMs and Causal World Models","created":"2024-10-25","cited_by_count":9,"area":"wm"},{"id":"2108.11684","title":"Disentangled Generative Models for Robust Prediction of System Dynamics","created":"2021-08-26","cited_by_count":8,"area":null},{"id":"2105.08970","title":"Disentanglement Learning for Variational Autoencoders Applied to Audio-Visual Speech Enhancement","created":"2021-05-19","cited_by_count":7,"area":null},{"id":"2503.22397","title":"GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model -- Bringing Motion Generation to the Clinical Domain","created":"2025-03-28","cited_by_count":7,"area":null},{"id":"2512.15134","title":"From Isolation to Entanglement: When Do Interpretability Methods Identify and Disentangle Known Concepts?","created":"2025-12-17","cited_by_count":7,"area":null},{"id":"2102.05507","title":"On Disentanglement in Gaussian Process Variational Autoencoders","created":"2021-02-10","cited_by_count":5,"area":null},{"id":"2205.10056","title":"Leveraging Relational Information for Learning Weakly Disentangled Representations","created":"2022-05-20","cited_by_count":5,"area":null},{"id":"2212.07699","title":"Retrieval-based Disentangled Representation Learning with Natural Language Supervision","created":"2022-12-15","cited_by_count":5,"area":null},{"id":"2106.12472","title":"Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders","created":"2021-06-23","cited_by_count":4,"area":null},{"id":"2110.03498","title":"On the relationship between disentanglement and multi-task learning","created":"2021-10-07","cited_by_count":4,"area":null},{"id":"2205.10866","title":"Blackbird's language matrices (BLMs): a new benchmark to investigate disentangled generalisation in neural networks","created":"2022-05-22","cited_by_count":4,"area":null},{"id":"2206.07196","title":"Towards a Solution to Bongard Problems: A Causal Approach","created":"2022-06-14","cited_by_count":4,"area":null},{"id":"2408.05599","title":"Sequential Representation Learning via Static-Dynamic Conditional Disentanglement","created":"2024-08-10","cited_by_count":4,"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":"2202.09926","title":"Disentangling Autoencoders (DAE)","created":"2022-02-20","cited_by_count":3,"area":null},{"id":"2206.01802","title":"Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength","created":"2022-06-03","cited_by_count":3,"area":null},{"id":"2502.13757","title":"Identifying Metric Structures of Deep Latent Variable Models","created":"2025-02-19","cited_by_count":3,"area":null},{"id":"2107.11857","title":"Improving Robot Localisation by Ignoring Visual Distraction","created":"2021-07-25","cited_by_count":2,"area":null},{"id":"2202.00391","title":"Right for the Right Latent Factors: Debiasing Generative Models via Disentanglement","created":"2022-02-01","cited_by_count":2,"area":null},{"id":"2204.07075","title":"Learning and controlling the source-filter representation of speech with a variational autoencoder","created":"2022-04-14","cited_by_count":2,"area":null},{"id":"2302.11737","title":"Causally Disentangled Generative Variational AutoEncoder","created":"2023-02-23","cited_by_count":2,"area":null},{"id":"2402.12767","title":"Nonstationary Time Series Forecasting via Unknown Distribution Adaptation","created":"2024-02-20","cited_by_count":2,"area":null},{"id":"2207.08050","title":"Repairing Systematic Outliers by Learning Clean Subspaces in VAEs","created":"2022-07-17","cited_by_count":1,"area":null},{"id":"2311.06567","title":"SCADI: Self-supervised Causal Disentanglement in Latent Variable Models","created":"2023-11-11","cited_by_count":1,"area":null},{"id":"2311.12461","title":"HiFi-Syn: Hierarchical Granularity Discrimination for High-Fidelity Synthesis of MR Images with Structure Preservation","created":"2023-11-21","cited_by_count":1,"area":null},{"id":"2405.16219","title":"Structural Disentanglement of Causal and Correlated Concepts","created":"2024-05-25","cited_by_count":1,"area":null},{"id":"2507.13934","title":"DiViD: Disentangled Video Diffusion for Static-Dynamic Factorization","created":"2025-07-18","cited_by_count":1,"area":"diffusion"},{"id":"2601.10107","title":"Enhancing Visual In-Context Learning by Multi-Faceted Fusion","created":"2026-01-15","cited_by_count":1,"area":null},{"id":"2112.10638","title":"Latte: Cross-framework Python Package for Evaluation of Latent-Based Generative Models","created":"2021-12-20","cited_by_count":0,"area":null},{"id":"2204.14189","title":"Oracle Guided Image Synthesis with Relative Queries","created":"2022-04-28","cited_by_count":0,"area":null},{"id":"2209.05336","title":"Modular Representations for Weak Disentanglement","created":"2022-09-12","cited_by_count":0,"area":null},{"id":"2209.06827","title":"Weakly Supervised Invariant Representation Learning Via Disentangling Known and Unknown Nuisance Factors","created":"2022-09-15","cited_by_count":0,"area":null},{"id":"2310.07683","title":"Controllable Data Generation Via Iterative Data-Property Mutual Mappings","created":"2023-10-11","cited_by_count":0,"area":null},{"id":"2310.09278","title":"Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning","created":"2023-10-13","cited_by_count":0,"area":null},{"id":"2402.15819","title":"Debiased Model-based Interactive Recommendation","created":"2024-02-24","cited_by_count":0,"area":null},{"id":"2502.03123","title":"Disentanglement in Difference: Directly Learning Semantically Disentangled Representations by Maximizing Inter-Factor Differences","created":"2025-02-05","cited_by_count":0,"area":null},{"id":"2602.00002","title":"Disentangled Interest Network for Out-of-Distribution CTR Prediction","created":"2025-11-14","cited_by_count":0,"area":null},{"id":"2605.20732","title":"Deep Attention Reweighting: Post-Hoc Attention-Based Feature Aggregation in CNNs for Disentangling Core and Spurious Features under Spurious Correlations","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2606.21385","title":"Unsupervised Disentanglement Without Compromises : How Functional Orthogonality Enforces Identifiability","created":"2026-06-19","cited_by_count":0,"area":null}],"next_cursor":null}