36 citations · 237 across the 33 of their papers we have counts for
4 papers · 1 filter
Effect of Optimizer, Initializer, and Architecture of Hypernetworks on Continual Learning from Demonstration
Sayantan Auddy, Sebastian Bergner, Justus Piater
In continual learning from demonstration (CLfD), a robot learns a sequence of real-world motion skills continually from human demonstrations. Recently, hypernetworks have been succ…
Scalable and Efficient Continual Learning from Demonstration via a Hypernetwork-generated Stable Dynamics Model
Sayantan Auddy, Jakob Hollenstein, Matteo Saveriano +2
Robots capable of learning from demonstration (LfD) must exhibit stability while executing learned motion skills. To be effective in the real world, they should also remember multi…
GRINN: A Physics-Informed Neural Network for solving hydrodynamic systems in the presence of self-gravity
Sayantan Auddy, Ramit Dey, Neal J. Turner +1
Modeling self-gravitating gas flows is essential to answering many fundamental questions in astrophysics. This spans many topics including planet-forming disks, star-forming clouds…
Predicting Stellar Mass Accretion: An Optimized Echo State Network Approach in Time Series Modeling
Gianfranco Bino, Shantanu Basu, Ramit Dey +3
Modeling the dynamics of the formation and evolution of protostellar disks as well as the history of stellar mass accretion typically involve the numerical solution of complex syst…