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pro vyhledávání: '"Skomski, Elliott"'
Autor:
Lee, Jung H, Kvinge, Henry J, Howland, Scott, New, Zachary, Buckheit, John, Phillips, Lauren A., Skomski, Elliott, Hibler, Jessica, Corley, Courtney D., Hodas, Nathan O.
Transfer learning (TL) leverages previously obtained knowledge to learn new tasks efficiently and has been used to train deep learning (DL) models with limited amount of data. When TL is applied to DL, pretrained (teacher) models are fine-tuned to bu
Externí odkaz:
http://arxiv.org/abs/2111.10937
We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multiparametric programming problems emerging from explicit nonlinear model pre
Externí odkaz:
http://arxiv.org/abs/2107.11843
Autor:
Kvinge, Henry, Howland, Scott, Courts, Nico, Phillips, Lauren A., Buckheit, John, New, Zachary, Skomski, Elliott, Lee, Jung H., Tiwari, Sandeep, Hibler, Jessica, Corley, Courtney D., Hodas, Nathan O.
The field of few-shot learning has made remarkable strides in developing powerful models that can operate in the small data regime. Nearly all of these methods assume every unlabeled instance encountered will belong to a handful of known classes for
Externí odkaz:
http://arxiv.org/abs/2106.01423
Autor:
Skomski, Elliott, Tuor, Aaron, Avila, Andrew, Phillips, Lauren, New, Zachary, Kvinge, Henry, Corley, Courtney D., Hodas, Nathan
Recently proposed few-shot image classification methods have generally focused on use cases where the objects to be classified are the central subject of images. Despite success on benchmark vision datasets aligned with this use case, these methods t
Externí odkaz:
http://arxiv.org/abs/2104.03496
Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics. This work explores a novel formulation for data-efficient learning of deep control-oriented nonlinear dynamical m
Externí odkaz:
http://arxiv.org/abs/2101.01864
Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physically-realistic, and data-efficient models. However, in the absence of
Externí odkaz:
http://arxiv.org/abs/2011.13497
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