33 citations · 34 across the 5 of their papers we have counts for
5 papers
pro-team at LLMs4OL 2026 Tasks Flagship and Reuse: Retrieval-Augmented Generation and Vocabulary-Constrained Filtering for Ontology Learning
Shivam Mishra, Dhannu Ram Meena, Muneendra Ojha +2
Ontology learning from text remains challenging despite significant progress in Large Language Models (LLMs), which can hallucinate domain terms, produce inconsistent formats, and…
FAM: fast adaptive federated meta-learning
Indrajeet Kumar Sinha, Shekhar Verma, Krishna Pratap Singh
In this work, we propose a fast adaptive federated meta-learning (FAM) framework for collaboratively learning a single global model, which can then be personalized locally on indiv…
DenseBAM-GI: Attention Augmented DeneseNet with momentum aided GRU for HMER
Aniket Pal, Krishna Pratap Singh
The task of recognising Handwritten Mathematical Expressions (HMER) is crucial in the fields of digital education and scholarly research. However, it is difficult to accurately det…
Learning to Learn with Indispensable Connections
Sambhavi Tiwari, Manas Gogoi, Shekhar Verma +1
Meta-learning aims to solve unseen tasks with few labelled instances. Nevertheless, despite its effectiveness for quick learning in existing optimization-based methods, it has seve…
Signature Verification using Geometrical Features and Artificial Neural Network Classifier
Anamika Jain, Satish Kumar Singh, Krishna Pratap Singh
Signature verification has been one of the major researched areas in the field of computer vision. Many financial and legal organizations use signature verification as access contr…