no code implementations • 10 Apr 2024 • Shrey Gupta, Yongbee Park, Jianzhao Bi, Suyash Gupta, Andreas Züfle, Avani Wildani, Yang Liu
We recognize this transfer problem as spatial transfer learning and propose a new feature named Latent Dependency Factor (LDF) that captures spatial and semantic dependencies of both domains and is subsequently added to the datasets.
1 code implementation • 8 Nov 2023 • Anmol Agarwal, Shrey Gupta, Vamshi Bonagiri, Manas Gaur, Joseph Reagle, Ponnurangam Kumaraguru
Information Disguise (ID), a part of computational ethics in Natural Language Processing (NLP), is concerned with best practices of textual paraphrasing to prevent the non-consensual use of authors' posts on the Internet.
1 code implementation • NAACL (CLPsych) 2022 • Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy, Vignesh Narayanan, Ponnurangam Kumaraguru, Amit Sheth
We demonstrate the challenge of using existing datasets to train a DLM for generating FQs that adhere to clinical process knowledge.
no code implementations • 26 Apr 2022 • Shrey Gupta, Jianzhao Bi, Yang Liu, Avani Wildani
Our methodology,~\us{}, is a boosting and random-forest based ensemble methodology that utilizes importance sampling to reduce the skewness due to the source dataset.
no code implementations • 29 Oct 2021 • Ashwin Singh, Mallika Subramanian, Anmol Agarwal, Pratyush Priyadarshi, Shrey Gupta, Kiran Garimella, Sanjeev Kumar, Ritesh Kumar, Lokesh Garg, Erica Arya, Ponnurangam Kumaraguru
Our classifier achieves accuracies ranging from 79% to 90% across the five states, demonstrating its potential for assisting future ethnographic investigations.