Zobrazeno 1 - 10
of 2 479
pro vyhledávání: '"P Tanuja"'
Autor:
Ganesan, Adithya V, Varadarajan, Vasudha, Lal, Yash Kumar, Eijsbroek, Veerle C., Kjell, Katarina, Kjell, Oscar N. E., Dhanasekaran, Tanuja, Stade, Elizabeth C., Eichstaedt, Johannes C., Boyd, Ryan L., Schwartz, H. Andrew, Flek, Lucie
Use of large language models such as ChatGPT (GPT-4) for mental health support has grown rapidly, emerging as a promising route to assess and help people with mood disorders, like depression. However, we have a limited understanding of GPT-4's schema
Externí odkaz:
http://arxiv.org/abs/2411.13800
LLM based copilot assistants are useful in everyday tasks. There is a proliferation in the exploration of AI assistant use cases to support radiology workflows in a reliable manner. In this work, we present RadPhi-3, a Small Language Model instructio
Externí odkaz:
http://arxiv.org/abs/2411.13604
Autor:
Gupta, Ravi Kant, Jindal, Mohit, Jain, Garima, Sridhar, Epari, Yadav, Subhash, Jain, Hasmukh, Shet, Tanuja, Sakhdeo, Uma, Sengar, Manju, Nayak, Lingaraj, Bagal, Bhausaheb, Apkare, Umesh, Sethi, Amit
We address the challenge of automated classification of diffuse large B-cell lymphoma (DLBCL) into its two primary subtypes: activated B-cell-like (ABC) and germinal center B-cell-like (GCB). Accurate classification between these subtypes is essentia
Externí odkaz:
http://arxiv.org/abs/2411.08531
Autor:
Wu, Nemin, Cao, Qian, Wang, Zhangyu, Liu, Zeping, Qi, Yanlin, Zhang, Jielu, Ni, Joshua, Yao, Xiaobai, Ma, Hongxu, Mu, Lan, Ermon, Stefano, Ganu, Tanuja, Nambi, Akshay, Lao, Ni, Mai, Gengchen
Spatial representation learning (SRL) aims at learning general-purpose neural network representations from various types of spatial data (e.g., points, polylines, polygons, networks, images, etc.) in their native formats. Learning good spatial repres
Externí odkaz:
http://arxiv.org/abs/2406.15658
Autor:
Wang, Hengyi, Shi, Haizhou, Tan, Shiwei, Qin, Weiyi, Wang, Wenyuan, Zhang, Tunyu, Nambi, Akshay, Ganu, Tanuja, Wang, Hao
Multimodal Large Language Models (MLLMs) have shown significant promise in various applications, leading to broad interest from researchers and practitioners alike. However, a comprehensive evaluation of their long-context capabilities remains undere
Externí odkaz:
http://arxiv.org/abs/2406.11230
Large language models (LLMs) have transformed AI across diverse domains, with prompting being central to their success in guiding model outputs. However, manual prompt engineering is both labor-intensive and domain-specific, necessitating the need fo
Externí odkaz:
http://arxiv.org/abs/2405.18369
Autor:
Kumar, Somnath, Balloli, Vaibhav, Ranjit, Mercy, Ahuja, Kabir, Ganu, Tanuja, Sitaram, Sunayana, Bali, Kalika, Nambi, Akshay
Large language models (LLMs) are at the forefront of transforming numerous domains globally. However, their inclusivity and effectiveness remain limited for non-Latin scripts and low-resource languages. This paper tackles the imperative challenge of
Externí odkaz:
http://arxiv.org/abs/2405.18359
Recent advancements in Multi-modal Large Language Models (MLLMs) have significantly improved their performance in tasks combining vision and language. However, challenges persist in detailed multi-modal understanding, comprehension of complex tasks,
Externí odkaz:
http://arxiv.org/abs/2405.18358
Small Language Models (SLMs) have shown remarkable performance in general domain language understanding, reasoning and coding tasks, but their capabilities in the medical domain, particularly concerning radiology text, is less explored. In this study
Externí odkaz:
http://arxiv.org/abs/2403.09725
Large Language Models (LLMs), excel in natural language understanding, but their capability for complex mathematical reasoning with an amalgamation of structured tables and unstructured text is uncertain. This study explores LLMs' mathematical reason
Externí odkaz:
http://arxiv.org/abs/2402.11194