Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Tejwani, Ravi"'
Recent advancements have enabled human-robot collaboration through physical assistance and verbal guidance. However, limitations persist in coordinating robots' physical motions and speech in response to real-time changes in human behavior during col
Externí odkaz:
http://arxiv.org/abs/2305.05456
Although telepresence assistive robots have made significant progress, they still lack the sense of realism and physical presence of the remote operator. This results in a lack of trust and adoption of such robots. In this paper, we introduce an Avat
Externí odkaz:
http://arxiv.org/abs/2303.02546
In this work, we compare different neural topic modeling methods in learning the topical propensities of different psychiatric conditions from the psychotherapy session transcripts parsed from speech recordings. We also incorporate temporal modeling
Externí odkaz:
http://arxiv.org/abs/2204.10189
Autor:
Tejwani, Ravi, Kuo, Yen-Ling, Shu, Tianmin, Stankovits, Bennett, Gutfreund, Dan, Tenenbaum, Joshua B., Katz, Boris, Barbu, Andrei
Much of what we do as humans is engage socially with other agents, a skill that robots must also eventually possess. We demonstrate that a rich theory of social interactions originating from microsociology and economics can be formalized by extending
Externí odkaz:
http://arxiv.org/abs/2110.10298
Publikováno v:
13th International Conference on Social Robotics (ICSR 2021)
Conversational AI agents are becoming ubiquitous and provide assistance to us in our everyday activities. In recent years, researchers have explored the migration of these agents across different embodiments in order to maintain the continuity of the
Externí odkaz:
http://arxiv.org/abs/2010.13319
The migration of conversational AI agents across different embodiments in order to maintain the continuity of the task has been recently explored to further improve user experience. However, these migratable agents lack contextual understanding of th
Externí odkaz:
http://arxiv.org/abs/2010.12091
Publikováno v:
Proceedings of the 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020
Conversational AI agents are proliferating, embodying a range of devices such as smart speakers, smart displays, robots, cars, and more. We can envision a future where a personal conversational agent could migrate across different form factors and en
Externí odkaz:
http://arxiv.org/abs/2007.05801
Autor:
Choromanska, Anna, Cowen, Benjamin, Kumaravel, Sadhana, Luss, Ronny, Rigotti, Mattia, Rish, Irina, Kingsbury, Brian, DiAchille, Paolo, Gurev, Viatcheslav, Tejwani, Ravi, Bouneffouf, Djallel
Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective function. State-of-the-art methods rely on error backpropagation, which suffers from several well-know
Externí odkaz:
http://arxiv.org/abs/1806.09077
The goal of the present study is to identify autism using machine learning techniques and resting-state brain imaging data, leveraging the temporal variability of the functional connections (FC) as the only information. We estimated and compared the
Externí odkaz:
http://arxiv.org/abs/1712.08041
Autor:
Arora, Nikita, Tejwani, Ravi, Solanki, Chetan Singh, Narayanan, N.C., Venkateswaran, Jayendran
Publikováno v:
In Energy Procedia December 2016 90:681-690
