21 citations · 34 across the 35 of their papers we have counts for
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cs.AI2025★ 1 cited
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…
cs.AI2025★ 1 cited
Bridging the Gap in XAI-Why Reliable Metrics Matter for Explainability and Compliance
Pratinav Seth, Vinay Kumar Sankarapu
Reliable explainability is not only a technical goal but also a cornerstone of private AI governance. As AI models enter high-stakes sectors, private actors such as auditors, insur…