2 citations · 2 across the 6 of their papers we have counts for
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PluRel-to-RDB-PFN: Schema-Guided Synthetic Relational Pretraining
Mohammad Sadeq Abolhasani, Viswanath Ganapathy
Relational Foundation Models (RFMs) require large-scale synthetic relational databases for pretraining, but existing approaches tightly couple data generation with the model traini…
Curriculum Matters: Data-Efficient Relational PFN Pretraining with Synthetic Data
Mohammad Sadeq Abolhasani, Viswanath Ganapathy
Relational Prior-Data Fitted Networks (PFNs) such as RDB-PFN approximate Bayesian inference over multi-table relational databases by pretraining on millions of synthetic tasks. We…
Beyond Predefined Schemas: TRACE-KG for Context-Enriched Knowledge Graph Generation
Mohammad Sadeq Abolhasani, Yang Ba, Yixuan He +1
Knowledge graph generation typically relies either on predefined ontologies or on schema-free extraction. Ontology-driven pipelines enforce consistent typing but require costly sch…
Measuring Dataset Diversity from a Geometric Perspective
Yang Ba, Mohammad Sadeq Abolhasani, Michelle V Mancenido +1
Diversity can be broadly defined as the presence of meaningful variation across elements, which can be viewed from multiple perspectives, including statistical variation and geomet…