From the 1 of 18 linked papers with an AI index.
2 citations · 3 across the 10 of their papers we have counts for
4 papers · 1 filter
SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching
Inwon Kang, Kavitha Srinivas, Nandana Mihindukulasooriya +4
Schema matching is a fundamental step in integrating heterogeneous data sources. While Pre-trained Language Models (PLMs) have revolutionized this task by capturing linguistic sema…
Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks
Liane Vogel, Kavitha Srinivas, Niharika D'Souza +3
Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as table retrieval, semantic sea…
TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes
Aamod Khatiwada, Harsha Kokel, Ibrahim Abdelaziz +7
Enterprises have a growing need to identify relevant tables in data lakes; e.g. tables that are unionable, joinable, or subsets of each other. Tabular neural models can be helpful…
MiGrATe: Mixed-Policy GRPO for Adaptation at Test-Time
Peter Phan, Dhruv Agarwal, Kavitha Srinivas +3
Large language models (LLMs) are increasingly being applied to black-box optimization tasks, from program synthesis to molecule design. Prior work typically leverages in-context le…