4 papers
Exploring Fine-Tuning for Tabular Foundation Models
Aditya Tanna, Pratinav Seth, Mohamed Bouadi +1
Tabular Foundation Models (TFMs) have recently shown strong in-context learning capabilities on structured data, achieving zero-shot performance comparable to traditional machine l…
TabTune: A Unified Library for Inference and Fine-Tuning Tabular Foundation Models
Aditya Tanna, Pratinav Seth, Mohamed Bouadi +2
Tabular foundation models represent a growing paradigm in structured data learning, extending the benefits of large-scale pretraining to tabular domains. However, their adoption re…
Orion-MSP: Multi-Scale Sparse Attention for Tabular In-Context Learning
Mohamed Bouadi, Pratinav Seth, Aditya Tanna +1
Tabular data remain the predominant format for real-world applications. Yet, developing effective neural models for tabular data remains challenging due to heterogeneous feature ty…
Diversity Augmentation of Dynamic User Preference Data for Boosting Personalized Text Summarizers
Parthiv Chatterjee, Shivam Sonawane, Amey Hengle +3
Document summarization enables efficient extraction of user-relevant content but is inherently shaped by individual subjectivity, making it challenging to identify subjective salie…