no code implementations • 4 Feb 2025 • Ravi Tejwani, Karl Velazquez, John Payne, Paolo Bonato, Harry Asada
A method for cross-modality embedding of force profile and words is presented for synergistic coordination of verbal and haptic communication.
no code implementations • 13 Apr 2022 • Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi, Ravi Tejwani
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.
no code implementations • 22 Oct 2020 • Ravi Tejwani, Boris Katz, Cynthia Breazeal
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.
no code implementations • 22 Oct 2020 • Ravi Tejwani, Boris Katz, Cynthia Breazeal
Conversational AI agents are becoming ubiquitous and provide assistance to us in our everyday activities.
1 code implementation • 24 Jun 2018 • Anna Choromanska, Benjamin Cowen, Sadhana Kumaravel, Ronny Luss, Mattia Rigotti, Irina Rish, Brian Kingsbury, Paolo DiAchille, Viatcheslav Gurev, Ravi Tejwani, Djallel Bouneffouf
Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective function.
no code implementations • 21 Dec 2017 • Ravi Tejwani, Adam Liska, Hongyuan You, Jenna Reinen, Payel Das
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.