{"total":14,"rows":[{"id":"2503.05954","title":"A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond","created":"2025-03-07","cited_by_count":15,"area":null},{"id":"2503.19466","title":"A Probabilistic Neuro-symbolic Layer for Algebraic Constraint Satisfaction","created":"2025-03-25","cited_by_count":11,"area":null},{"id":"2507.19334","title":"Doubling Your Data in Minutes: Ultra-fast Tabular Data Generation via LLM-Induced Dependency Graphs","created":"2025-07-25","cited_by_count":9,"area":null},{"id":"2507.11127","title":"Defining neurosymbolic AI","created":"2025-07-15","cited_by_count":8,"area":null},{"id":"2601.03839","title":"Logic Tensor Network-Enhanced Generative Adversarial Network","created":"2026-01-07","cited_by_count":3,"area":null},{"id":"2505.00466","title":"A General Framework for Property-Driven Machine Learning","created":"2025-05-01","cited_by_count":1,"area":null},{"id":"2510.04855","title":"Synthesising Counterfactual Explanations via Label-Conditional Gaussian Mixture Variational Autoencoders","created":"2025-10-06","cited_by_count":1,"area":null},{"id":"2602.08681","title":"The Theory and Practice of MAP Inference over Non-Convex Constraints","created":"2026-02-09","cited_by_count":1,"area":null},{"id":"2603.23016","title":"A Sobering Look at Tabular Data Generation via Probabilistic Circuits","created":"2026-03-24","cited_by_count":1,"area":null},{"id":"2604.24368","title":"SAGE: Sparse Adaptive Guidance for Dependency-Aware Tabular Data Generation","created":"2026-04-27","cited_by_count":1,"area":null},{"id":"2509.09960","title":"Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes","created":"2025-09-12","cited_by_count":0,"area":null},{"id":"2605.13845","title":"Quantitative Linear Logic for Neuro-Symbolic Learning and Verification","created":"2026-05-13","cited_by_count":0,"area":null},{"id":"2605.30456","title":"DisjunctiveNet: Neural Symbolic Learning via Differentiable Convexified Optimization Layers","created":"2026-05-28","cited_by_count":0,"area":null},{"id":"2608.21605","title":"Generate in the Chart, Not on the Boundary: Function-Symbol Grounding for Hard Constraints in LTN-GANs","created":"2026-08-21","cited_by_count":0,"area":null}],"next_cursor":null}