Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Jain, Raghav"'
Autor:
Ranka, Hriday, Surana, Mokshit, Kothari, Neel, Pariawala, Veer, Banerjee, Pratyay, Surve, Aditya, Sankepally, Sainath Reddy, Jain, Raghav, Lalwani, Jhagrut, Mehta, Swapneel
It is becoming cheaper to launch disinformation operations at scale using AI-generated content, in particular 'deepfake' technology. We have observed instances of deepfakes in political campaigns, where generated content is employed to both bolster t
Externí odkaz:
http://arxiv.org/abs/2406.14290
Autor:
Jha, Prince, Jain, Raghav, Mandal, Konika, Chadha, Aman, Saha, Sriparna, Bhattacharyya, Pushpak
In the digital world, memes present a unique challenge for content moderation due to their potential to spread harmful content. Although detection methods have improved, proactive solutions such as intervention are still limited, with current researc
Externí odkaz:
http://arxiv.org/abs/2406.05344
Measuring nonlinear feature interaction is an established approach to understanding complex patterns of attribution in many models. In this paper, we use Shapley Taylor interaction indices (STII) to analyze the impact of underlying data structure on
Externí odkaz:
http://arxiv.org/abs/2403.13106
Autor:
Ghosh, Akash, Acharya, Arkadeep, Jha, Prince, Gaudgaul, Aniket, Majumdar, Rajdeep, Saha, Sriparna, Chadha, Aman, Jain, Raghav, Sinha, Setu, Agarwal, Shivani
In the healthcare domain, summarizing medical questions posed by patients is critical for improving doctor-patient interactions and medical decision-making. Although medical data has grown in complexity and quantity, the current body of research in t
Externí odkaz:
http://arxiv.org/abs/2401.01596
In the era of modern healthcare, swiftly generating medical question summaries is crucial for informed and timely patient care. Despite the increasing complexity and volume of medical data, existing studies have focused solely on text-based summariza
Externí odkaz:
http://arxiv.org/abs/2312.11541
Autor:
Nay, John J., Karamardian, David, Lawsky, Sarah B., Tao, Wenting, Bhat, Meghana, Jain, Raghav, Lee, Aaron Travis, Choi, Jonathan H., Kasai, Jungo
Better understanding of Large Language Models' (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence, and leveraging LLMs to identify inconsistencies in law. This paper explore
Externí odkaz:
http://arxiv.org/abs/2306.07075
The internet has had a dramatic effect on the healthcare industry, allowing documents to be saved, shared, and managed digitally. This has made it easier to locate and share important data, improving patient care and providing more opportunities for
Externí odkaz:
http://arxiv.org/abs/2212.01669
The task of automatic text summarization has gained a lot of traction due to the recent advancements in machine learning techniques. However, evaluating the quality of a generated summary remains to be an open problem. The literature has widely adopt
Externí odkaz:
http://arxiv.org/abs/2201.09282
The anthology of spoken languages today is inundated with textual information, necessitating the development of automatic summarization models. In this manuscript, we propose an extractor-paraphraser based abstractive summarization system that exploi
Externí odkaz:
http://arxiv.org/abs/2105.01296
Autor:
Green, Cara L., Trautman, Michaela E., Chaiyakul, Krittisak, Jain, Raghav, Alam, Yasmine H., Babygirija, Reji, Pak, Heidi H., Sonsalla, Michelle M., Calubag, Mariah F., Yeh, Chung-Yang, Bleicher, Anneliese, Novak, Grace, Liu, Teresa T., Newman, Sarah, Ricke, Will A., Matkowskyj, Kristina A., Ong, Irene M., Jang, Cholsoon, Simcox, Judith, Lamming, Dudley W.
Publikováno v:
In Cell Metabolism 7 November 2023 35(11):1976-1995
