Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Raglin, Adrienne"'
Autor:
Hossain, Jumman, Dey, Emon, Chugh, Snehalraj, Ahmed, Masud, Anwar, MS, Faridee, Abu-Zaher, Hoppes, Jason, Trout, Theron, Basak, Anjon, Chowdhury, Rafidh, Mistry, Rishabh, Kim, Hyun, Freeman, Jade, Suri, Niranjan, Raglin, Adrienne, Busart, Carl, Gregory, Timothy, Ravi, Anuradha, Roy, Nirmalya
The increasing deployment of autonomous systems in complex environments necessitates efficient communication and task completion among multiple agents. This paper presents SERN (Simulation-Enhanced Realistic Navigation), a novel framework integrating
Externí odkaz:
http://arxiv.org/abs/2410.16686
Machine learning models rely on various assumptions to attain high accuracy. One of the preliminary assumptions of these models is the independent and identical distribution, which suggests that the train and test data are sampled from the same distr
Externí odkaz:
http://arxiv.org/abs/2209.15177
Deployment of Internet of Things (IoT) devices and Data Fusion techniques have gained popularity in public and government domains. This usually requires capturing and consolidating data from multiple sources. As datasets do not necessarily originate
Externí odkaz:
http://arxiv.org/abs/2106.01497
Autor:
Sharma, Piyush K., Raglin, Adrienne
The military is investigating methods to improve communication and agility in its multi-domain operations (MDO). Nascent popularity of Internet of Things (IoT) has gained traction in public and government domains. Its usage in MDO may revolutionize f
Externí odkaz:
http://arxiv.org/abs/2106.00787
Autor:
Moraffah, Raha, Sheth, Paras, Karami, Mansooreh, Bhattacharya, Anchit, Wang, Qianru, Tahir, Anique, Raglin, Adrienne, Liu, Huan
Time series data is a collection of chronological observations which is generated by several domains such as medical and financial fields. Over the years, different tasks such as classification, forecasting, and clustering have been proposed to analy
Externí odkaz:
http://arxiv.org/abs/2102.05829
We propose a generative Causal Adversarial Network (CAN) for learning and sampling from conditional and interventional distributions. In contrast to the existing CausalGAN which requires the causal graph to be given, our proposed framework learns the
Externí odkaz:
http://arxiv.org/abs/2008.11376
Machine learning models have had discernible achievements in a myriad of applications. However, most of these models are black-boxes, and it is obscure how the decisions are made by them. This makes the models unreliable and untrustworthy. To provide
Externí odkaz:
http://arxiv.org/abs/2003.03934
Studies show that the representations learned by deep neural networks can be transferred to similar prediction tasks in other domains for which we do not have enough labeled data. However, as we transition to higher layers in the model, the represent
Externí odkaz:
http://arxiv.org/abs/1910.12417
Autor:
Schwartz, Peter J., Jensen, Benjamin, Hohil, Myron E., Nyarko, Kofi, Taiwo, Peter, Nwachukwu, Kelechi, Rawal, Justine, Richardson, John, Raglin, Adrienne
Publikováno v:
Proceedings of SPIE; June 2024, Vol. 13051 Issue: 1 p130510L-130510L-20, 12920511p
Autor:
Schwartz, Peter J., Jensen, Benjamin, Hohil, Myron E., Rawal, Atul, Raglin, Adrienne, Wang, Qianlong, Tang, Ziying
Publikováno v:
Proceedings of SPIE; June 2024, Vol. 13051 Issue: 1 p130510J-130510J-7, 1174598p