21 citations · 34 across the 35 of their papers we have counts for
8 papers · 1 filter
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-Bix: Bi-Axial Attention for Tabular In-Context Learning
Mohamed Bouadi, Pratinav Seth, Aditya Tanna +1
Tabular data drive most real-world machine learning applications, yet building general-purpose models for them remains difficult. Mixed numeric and categorical fields, weak feature…
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…
Interpretability as Alignment: Making Internal Understanding a Design Principle
Aadit Sengupta, Pratinav Seth, Vinay Kumar Sankarapu
Frontier AI systems require governance mechanisms that can verify internal alignment, not just behavioral compliance. Private governance mechanisms audits, certification, insurance…
Interpretability-Aware Pruning for Efficient Medical Image Analysis
Nikita Malik, Pratinav Seth, Neeraj Kumar Singh +2
Deep learning has driven significant advances in medical image analysis, yet its adoption in clinical practice remains constrained by the large size and lack of transparency in mod…
SELF-PERCEPT: Introspection Improves Large Language Models' Detection of Multi-Person Mental Manipulation in Conversations
Danush Khanna, Pratinav Seth, Sidhaarth Sredharan Murali +5
Mental manipulation is a subtle yet pervasive form of abuse in interpersonal communication, making its detection critical for safeguarding potential victims. However, due to manipu…
xai_evals : A Framework for Evaluating Post-Hoc Local Explanation Methods
Pratinav Seth, Yashwardhan Rathore, Neeraj Kumar Singh +2
The growing complexity of machine learning and deep learning models has led to an increased reliance on opaque "black box" systems, making it difficult to understand the rationale…
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…