6 citations · 9 across the 8 of their papers we have counts for
6 papers
Deep Learning Explicit Differentiable Predictive Control Laws for Buildings
Jan Drgona, Aaron Tuor, Soumya Vasisht +2
We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multipara…
Prototypical Region Proposal Networks for Few-Shot Localization and Classification
Elliott Skomski, Aaron Tuor, Andrew Avila +5
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…
Constrained Block Nonlinear Neural Dynamical Models
Elliott Skomski, Soumya Vasisht, Colby Wight +3
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…
Physics-Informed Neural State Space Models via Learning and Evolution
Elliott Skomski, Jan Drgona, Aaron Tuor
Recent works exploring deep learning application to dynamical systems modeling have demonstrated that embedding physical priors into neural networks can yield more effective, physi…
Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems
Jan Drgona, Karol Kis, Aaron Tuor +2
We present differentiable predictive control (DPC) as a deep learning-based alternative to the explicit model predictive control (MPC) for unknown nonlinear systems. In the DPC fra…
Fuzzy Simplicial Networks: A Topology-Inspired Model to Improve Task Generalization in Few-shot Learning
Henry Kvinge, Zachary New, Nico Courts +6
Deep learning has shown great success in settings with massive amounts of data but has struggled when data is limited. Few-shot learning algorithms, which seek to address this limi…