{"total":43,"rows":[{"id":"2107.10098","title":"Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA","created":"2021-07-21","cited_by_count":160,"area":null},{"id":"2108.12043","title":"Learning Disentangled Representations in the Imaging Domain","created":"2021-08-26","cited_by_count":46,"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":"2211.03553","title":"Learning Causal Representations of Single Cells via Sparse Mechanism Shift Modeling","created":"2022-11-07","cited_by_count":34,"area":null},{"id":"2311.00866","title":"Generalizing Nonlinear ICA Beyond Structural Sparsity","created":"2023-11-01","cited_by_count":34,"area":null},{"id":"2207.07732","title":"Partial Disentanglement via Mechanism Sparsity","created":"2022-07-15","cited_by_count":29,"area":null},{"id":"2104.05622","title":"Where and What? Examining Interpretable Disentangled Representations","created":"2021-04-07","cited_by_count":27,"area":null},{"id":"2203.13751","title":"Efficient-VDVAE: Less is more","created":"2022-03-25","cited_by_count":26,"area":null},{"id":"2210.01078","title":"Unsupervised Model Selection for Time-series Anomaly Detection","created":"2022-10-03","cited_by_count":25,"area":null},{"id":"2211.01834","title":"Toward Unsupervised Outlier Model Selection","created":"2022-11-03","cited_by_count":19,"area":null},{"id":"2111.12448","title":"3D Shape Variational Autoencoder Latent Disentanglement via Mini-Batch Feature Swapping for Bodies and Faces","created":"2021-11-24","cited_by_count":15,"area":null},{"id":"2204.02283","title":"Lost in Latent Space: Disentangled Models and the Challenge of Combinatorial Generalisation","created":"2022-04-05","cited_by_count":11,"area":null},{"id":"2205.08399","title":"How do Variational Autoencoders Learn? Insights from Representational Similarity","created":"2022-05-17","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":"2305.13189","title":"Unsupervised Anomaly Detection with Rejection","created":"2023-05-22","cited_by_count":9,"area":null},{"id":"2310.11431","title":"Identifying Interpretable Visual Features in Artificial and Biological Neural Systems","created":"2023-10-17","cited_by_count":9,"area":null},{"id":"2504.13101","title":"Position: An Empirically Grounded Identifiability Theory Will Accelerate Self-Supervised Learning Research","created":"2025-04-17","cited_by_count":9,"area":null},{"id":"2104.01422","title":"A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?","created":"2021-04-03","cited_by_count":8,"area":null},{"id":"2210.09535","title":"Graph Anomaly Detection with Unsupervised GNNs","created":"2022-10-18","cited_by_count":8,"area":null},{"id":"2303.01384","title":"DAVA: Disentangling Adversarial Variational Autoencoder","created":"2023-03-02","cited_by_count":8,"area":null},{"id":"2307.10529","title":"Fast Unsupervised Deep Outlier Model Selection with Hypernetworks","created":"2023-07-20","cited_by_count":6,"area":null},{"id":"2102.06822","title":"Demystifying Inductive Biases for $\\beta$-VAE Based Architectures","created":"2021-02-12","cited_by_count":5,"area":null},{"id":"2111.05986","title":"SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision","created":"2021-11-10","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":"2112.08830","title":"Graph-wise Common Latent Factor Extraction for Unsupervised Graph Representation Learning","created":"2021-12-16","cited_by_count":3,"area":null},{"id":"2305.06886","title":"A Category-theoretical Meta-analysis of Definitions of Disentanglement","created":"2023-05-11","cited_by_count":3,"area":null},{"id":"2410.02136","title":"Disentangled Representation Learning for Parametric Partial Differential Equations","created":"2024-10-03","cited_by_count":3,"area":null},{"id":"2210.01760","title":"Evaluating Disentanglement in Generative Models Without Knowledge of Latent Factors","created":"2022-10-04","cited_by_count":2,"area":null},{"id":"2308.14380","title":"Self-Supervision for Tackling Unsupervised Anomaly Detection: Pitfalls and Opportunities","created":"2023-08-28","cited_by_count":2,"area":null},{"id":"2405.21042","title":"Comparing the information content of probabilistic representation spaces","created":"2024-05-31","cited_by_count":2,"area":null},{"id":"2410.19426","title":"Analyzing Generative Models by Manifold Entropic Metrics","created":"2024-10-25","cited_by_count":2,"area":null},{"id":"2412.18743","title":"Successes and Limitations of Object-centric Models at Compositional Generalisation","created":"2024-12-25","cited_by_count":2,"area":null},{"id":"2303.12959","title":"Variantional autoencoder with decremental information bottleneck for disentanglement","created":"2023-03-22","cited_by_count":1,"area":null},{"id":"2312.06932","title":"Predictive variational autoencoder for learning robust representations of time-series data","created":"2023-12-12","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":"2308.10708","title":"Measuring the Effect of Causal Disentanglement on the Adversarial Robustness of Neural Network Models","created":"2023-08-21","cited_by_count":0,"area":null},{"id":"2402.07845","title":"Unsupervised Optimisation of GNNs for Node Clustering","created":"2024-02-12","cited_by_count":0,"area":null},{"id":"2410.03972","title":"Measuring and Controlling Solution Degeneracy across Task-Trained Recurrent Neural Networks","created":"2024-10-04","cited_by_count":0,"area":null},{"id":"2501.14694","title":"Towards Automated Self-Supervised Learning for Truly Unsupervised Graph Anomaly Detection","created":"2025-01-24","cited_by_count":0,"area":null},{"id":"2502.20499","title":"Data Distributional Properties As Inductive Bias for Systematic Generalization","created":"2025-02-27","cited_by_count":0,"area":null},{"id":"2602.00208","title":"Analyzing Shapley Additive Explanations to Understand Anomaly Detection Algorithm Behaviors and Their Complementarity","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2603.11790","title":"Disentangled Representation Learning through Unsupervised Symmetry Group Discovery","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2608.30124","title":"TPR-Attention for Combinatorial Generalization","created":"2026-08-31","cited_by_count":0,"area":null}],"next_cursor":null}