arXiv (All)
open access
Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data
Synthetic sequential tabular data are increasingly used for privacy-preserving data sharing and research, yet conventional tabular metrics often overlook temporal structure. Existing single-table and relational evaluation protocols largely collapse records into static distributions, leaving key temp
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Proposed Data Sharing Activity#1052855
- Volley Revolver: A Novel Matrix-Encoding Method for Privacy-Preserving Neural Networks (Inference)#144166
- Do Temporal Link Predictors Need Learned Memory? A Smoothed-Count Baseline with a Handful of Parameters#1129956
- Is External Database Protection Static in Retrieval-Augmented Generation? Rethinking Privacy Preservation under Dynamic Queries#373328
- Modeling the Potential Effects of Historically Formed Relations in the Generative Relational Metamodel#692616
Record · ID 799005
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive