Zobrazeno 1 - 10
of 10 511
pro vyhledávání: '"Chaudhari P"'
Autor:
Pawan Kumar Singh, Vinod Kumar Sharma, Manjunath B Govindagoudar, Athul Thulasi, Dhruva Chaudhry, Chaudhari Pramod Shriram, Lokesh Kumar Lalwani, Aman Ahuja
Publikováno v:
BMJ Open Respiratory Research, Vol 10, Iss 1 (2023)
Background Patients with chronic-obstructive-pulmonary-disease (COPD) undergo bronchoscopy for various reasons, and are at relatively higher risk of complications. This study evaluated the efficacy of non-invasive ventilation (NIV) and high-flow-oxyg
Externí odkaz:
https://doaj.org/article/e7bf46f7f61d477bbbce53b294b2d4e6
Publikováno v:
EPJ Web of Conferences, Vol 295, p 09015 (2024)
Deep learning techniques have been proven to provide excellent performance for a variety of high-energy physics applications, such as particle identification, event reconstruction and trigger operations. Recently, we developed an end-to-end deep lear
Externí odkaz:
https://doaj.org/article/5f2ce1dd9ea14893af64f51fad37f5a2
Autor:
Shao, Yifei Simon, Li, Tianyu, Keyvanian, Shafagh, Chaudhari, Pratik, Kumar, Vijay, Figueroa, Nadia
Constraint-aware estimation of human intent is essential for robots to physically collaborate and interact with humans. Further, to achieve fluid collaboration in dynamic tasks intent estimation should be achieved in real-time. In this paper, we pres
Externí odkaz:
http://arxiv.org/abs/2409.00215
Providing security for information is highly critical in the current era with devices enabled with smart technology, where assuming a day without the internet is highly impossible. Fast internet at a cheaper price, not only made communication easy fo
Externí odkaz:
http://arxiv.org/abs/2408.12866
Recent advances in machine learning have significantly improved prediction accuracy in various applications. However, ensuring the calibration of probabilistic predictions remains a significant challenge. Despite efforts to enhance model calibration,
Externí odkaz:
http://arxiv.org/abs/2408.08998
Classical optimization and learning-based methods are the two reigning paradigms in deformable image registration. While optimization-based methods boast generalizability across modalities and robust performance, learning-based methods promise peak p
Externí odkaz:
http://arxiv.org/abs/2408.05839
Autor:
Samuel, Vinay, Zou, Henry Peng, Zhou, Yue, Chaudhari, Shreyas, Kalyan, Ashwin, Rajpurohit, Tanmay, Deshpande, Ameet, Narasimhan, Karthik, Murahari, Vishvak
Persona agents, which are LLM agents that act according to an assigned persona, have demonstrated impressive contextual response capabilities across various applications. These persona agents offer significant enhancements across diverse sectors, suc
Externí odkaz:
http://arxiv.org/abs/2407.18416
Autor:
Ramesh, Rahul, Bisulco, Anthony, DiTullio, Ronald W., Wei, Linran, Balasubramanian, Vijay, Daniilidis, Kostas, Chaudhari, Pratik
We show that many perception tasks, from visual recognition, semantic segmentation, optical flow, depth estimation to vocalization discrimination, are highly redundant functions of their input data. Images or spectrograms, projected into different su
Externí odkaz:
http://arxiv.org/abs/2407.13841
Autor:
Chintapalli, Sai Spandana, Wang, Rongguang, Yang, Zhijian, Tassopoulou, Vasiliki, Yu, Fanyang, Bashyam, Vishnu, Erus, Guray, Chaudhari, Pratik, Shou, Haochang, Davatzikos, Christos
Availability of large and diverse medical datasets is often challenged by privacy and data sharing restrictions. For successful application of machine learning techniques for disease diagnosis, prognosis, and precision medicine, large amounts of data
Externí odkaz:
http://arxiv.org/abs/2407.12897
Autor:
Eichermüller, Lena, Chaudhari, Gaurang, Katsavounidis, Ioannis, Lei, Zhijun, Tmar, Hassene, Herglotz, Christian, Kaup, André
Enabling high compression efficiency while keeping encoding energy consumption at a low level, requires prioritization of which videos need more sophisticated encoding techniques. However, the effects vary highly based on the content, and information
Externí odkaz:
http://arxiv.org/abs/2407.05900