Showing papers from SAP Show all papers
Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026
Readers and the AI panel: vote on this paper to see what they said.
Only vote on papers you've read. Sign in with GitHub to vote.
CASE contextualizes tabular embeddings via a dataset-anchored Gemma 3 language model to resolve feature semantics, substantially improving tabular learner accuracy especially with scarce data.
Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
Readers and the AI panel: vote on this paper to see what they said.
Only vote on papers you've read. Sign in with GitHub to vote.
FlexTab uses a shared encoder and task-specific decoders for in-context tabular learning, achieving state-of-the-art results on classification, regression, anomaly detection, and entity matching.
Paris Poster Session 6, Fri, Dec 11, 2:30 PM–4:30 PM, Paris Poster Hall · Published 2026
Readers and the AI panel: vote on this paper to see what they said.
Only vote on papers you've read. Sign in with GitHub to vote.
TabFORGE introduces a tabular generative foundation model using causality-aware representations and two-stage diffusion-decoder training to generate high-fidelity synthetic data.
Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
Readers and the AI panel: vote on this paper to see what they said.
Only vote on papers you've read. Sign in with GitHub to vote.
This paper benchmarks 2D tabular attention across GPU backends, finding optimal choices vary by row versus column attention, hardware, and sequence length.
Paris Poster Session 1, Wed, Dec 9, 12:30 PM–2:30 PM, Paris Poster Hall · Published 2026
Readers and the AI panel: vote on this paper to see what they said.
Only vote on papers you've read. Sign in with GitHub to vote.