Zobrazeno 1 - 10
of 801
pro vyhledávání: '"A. Ganu"'
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
Autor:
Nambi, Akshay, Balloli, Vaibhav, Ranjit, Mercy, Ganu, Tanuja, Ahuja, Kabir, Sitaram, Sunayana, Bali, Kalika
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/2305.17740
We propose Retrieval Augmented Generation (RAG) as an approach for automated radiology report writing that leverages multimodally aligned embeddings from a contrastively pretrained vision language model for retrieval of relevant candidate radiology t
Externí odkaz:
http://arxiv.org/abs/2305.03660
Autor:
Ahuja, Kabir, Diddee, Harshita, Hada, Rishav, Ochieng, Millicent, Ramesh, Krithika, Jain, Prachi, Nambi, Akshay, Ganu, Tanuja, Segal, Sameer, Axmed, Maxamed, Bali, Kalika, Sitaram, Sunayana
Generative AI models have shown impressive performance on many Natural Language Processing tasks such as language understanding, reasoning, and language generation. An important question being asked by the AI community today is about the capabilities
Externí odkaz:
http://arxiv.org/abs/2303.12528