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pro vyhledávání: '"P. Jithin"'
The exponential increase in Internet of Things (IoT) devices coupled with 6G pushing towards higher data rates and connected devices has sparked a surge in data. Consequently, harnessing the full potential of data-driven machine learning has become o
Externí odkaz:
http://arxiv.org/abs/2405.17309
Autor:
Gomez, Catalina, Wang, Ruolin, Breininger, Katharina, Casey, Corinne, Bradley, Chris, Pavlak, Mitchell, Pham, Alex, Yohannan, Jithin, Unberath, Mathias
Primary care providers are vital for initial triage and referrals to specialty care. In glaucoma, asymptomatic and fast progression can lead to vision loss, necessitating timely referrals to specialists. However, primary eye care providers may not id
Externí odkaz:
http://arxiv.org/abs/2407.11974
An experimental investigation was carried out to study heat transfer rates in a high-temperature, high-pressure region generated using the shock focusing technique. A shock tube test facility with a specially designed spherically converging test sect
Externí odkaz:
http://arxiv.org/abs/2405.12158
Intellecta Cognitiva: A Comprehensive Dataset for Advancing Academic Knowledge and Machine Reasoning
Intellecta dataset emerges as an innovative synthetic dataset, engineered to enhance the cognitive processing capabilities of contemporary language models. With a composition of 11.53 billion tokens, integrating 8.01 billion tokens of synthetic data
Externí odkaz:
http://arxiv.org/abs/2404.13065
Consider a binary statistical hypothesis testing problem, where $n$ independent and identically distributed random variables $Z^n$ are either distributed according to the null hypothesis $P$ or the alternative hypothesis $Q$, and only $P$ is known. A
Externí odkaz:
http://arxiv.org/abs/2403.03537
In recent years, large language models have demonstrated remarkable performance across various natural language processing (NLP) tasks. However, deploying these models for real-world applications often requires efficient inference solutions to handle
Externí odkaz:
http://arxiv.org/abs/2406.07553
Autor:
Xiong, Yifan, Jiang, Yuting, Yang, Ziyue, Qu, Lei, Zhao, Guoshuai, Liu, Shuguang, Zhong, Dong, Pinzur, Boris, Zhang, Jie, Wang, Yang, Jose, Jithin, Pourreza, Hossein, Baxter, Jeff, Datta, Kushal, Ram, Prabhat, Melton, Luke, Chau, Joe, Cheng, Peng, Xiong, Yongqiang, Zhou, Lidong
Reliability in cloud AI infrastructure is crucial for cloud service providers, prompting the widespread use of hardware redundancies. However, these redundancies can inadvertently lead to hidden degradation, so called "gray failure", for AI workloads
Externí odkaz:
http://arxiv.org/abs/2402.06194
We introduce RAGAs (Retrieval Augmented Generation Assessment), a framework for reference-free evaluation of Retrieval Augmented Generation (RAG) pipelines. RAG systems are composed of a retrieval and an LLM based generation module, and provide LLMs
Externí odkaz:
http://arxiv.org/abs/2309.15217
We consider a star-network of $n=n_0+n_p$ spin-$\frac{1}{2}$ particles, where interaction between $n_0$ central spins and $n_p$ peripheral spins are of the XYZ-type. In the limit $n_0/n_p\ll 1$, we show that for odd $n$, the ground state is doubly de
Externí odkaz:
http://arxiv.org/abs/2307.15949
Autor:
Jithin Thekkelkuthiyathottil Joseph, Ashok Jammigumpula, Jithin Jaise, Prathvi Naik, Abhiram N. Purohith, Sonia Shenoy, Samir Kumar Praharaj
Publikováno v:
Indian Journal of Psychiatry, Vol 66, Iss 8, Pp 759-761 (2024)
Externí odkaz:
https://doaj.org/article/87e068a7a2934ec39bef73338bf247b3