Zobrazeno 1 - 10
of 13 082
pro vyhledávání: '"A. Raha"'
We investigate the multi-channel 4-body scattering system using regularized 2- and 3-body contact interactions. The analysis determines the sensitivity of bound-state energies, scattering phase shifts and cross sections on the cutoff parameter ($\lam
Externí odkaz:
http://arxiv.org/abs/2411.00386
Autor:
Abdul, Wadood M, Pimentel, Marco AF, Salman, Muhammad Umar, Raha, Tathagata, Christophe, Clément, Kanithi, Praveen K, Hayat, Nasir, Rajan, Ronnie, Khan, Shadab
This technical report introduces a Named Clinical Entity Recognition Benchmark for evaluating language models in healthcare, addressing the crucial natural language processing (NLP) task of extracting structured information from clinical narratives t
Externí odkaz:
http://arxiv.org/abs/2410.05046
The Brill-Noether theory of curves plays a fundamental role in the theory of curves and their moduli and has been intensively studied since the 19th century. In contrast, Brill-Noether theory for higher dimensional varieties is less understood. It is
Externí odkaz:
http://arxiv.org/abs/2409.18008
Autor:
Christophe, Clément, Raha, Tathagata, Maslenkova, Svetlana, Salman, Muhammad Umar, Kanithi, Praveen K, Pimentel, Marco AF, Khan, Shadab
Large Language Models (LLMs) have demonstrated significant potential in transforming clinical applications. In this study, we investigate the efficacy of four techniques in adapting LLMs for clinical use-cases: continuous pretraining, instruct fine-t
Externí odkaz:
http://arxiv.org/abs/2409.14988
In this study, we explore the efficacy of advanced pre-trained architectures, such as Vision Transformers (ViT), ConvNeXt, and Swin Transformers in enhancing Federated Domain Generalization. These architectures capture global contextual features and
Externí odkaz:
http://arxiv.org/abs/2409.13527
Autor:
Kanithi, Praveen K, Christophe, Clément, Pimentel, Marco AF, Raha, Tathagata, Saadi, Nada, Javed, Hamza, Maslenkova, Svetlana, Hayat, Nasir, Rajan, Ronnie, Khan, Shadab
The rapid development of Large Language Models (LLMs) for healthcare applications has spurred calls for holistic evaluation beyond frequently-cited benchmarks like USMLE, to better reflect real-world performance. While real-world assessments are valu
Externí odkaz:
http://arxiv.org/abs/2409.07314
Med42-v2 introduces a suite of clinical large language models (LLMs) designed to address the limitations of generic models in healthcare settings. These models are built on Llama3 architecture and fine-tuned using specialized clinical data. They unde
Externí odkaz:
http://arxiv.org/abs/2408.06142
Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks
Autor:
Pimentel, Marco AF, Christophe, Clément, Raha, Tathagata, Munjal, Prateek, Kanithi, Praveen K, Khan, Shadab
As large language models (LLMs) continue to evolve, the need for robust and standardized evaluation benchmarks becomes paramount. Evaluating the performance of these models is a complex challenge that requires careful consideration of various linguis
Externí odkaz:
http://arxiv.org/abs/2407.21072
Autor:
Tan, Zhen, Zhao, Chengshuai, Moraffah, Raha, Li, Yifan, Wang, Song, Li, Jundong, Chen, Tianlong, Liu, Huan
Retrieval-Augmented Generative (RAG) models enhance Large Language Models (LLMs) by integrating external knowledge bases, improving their performance in applications like fact-checking and information searching. In this paper, we demonstrate a securi
Externí odkaz:
http://arxiv.org/abs/2406.19417
Advancements in 6G wireless technology have elevated the importance of beamforming, especially for attaining ultra-high data rates via millimeter-wave (mmWave) frequency deployment. Although promising, mmWave bands require substantial beam training t
Externí odkaz:
http://arxiv.org/abs/2406.02000