2 citations · 2 across the 13 of their papers we have counts for
6 papers · 1 filter
Learning Generalizable Neural Operators for Inverse Problems
Adam J. Thorpe, Stepan Tretiakov, Dibakar Roy Sarkar +2
Inverse problems challenge existing neural operator architectures because ill-posed inverse maps violate continuity, uniqueness, and stability assumptions. We introduce B2B${}^{-1}…
Zero-Shot Function Encoder-Based Differentiable Predictive Control
Hassan Iqbal, Xingjian Li, Tyler Ingebrand +4
We introduce a differentiable framework for zero-shot adaptive control over parametric families of nonlinear dynamical systems. Our approach integrates a function encoder-based neu…
Function Spaces Without Kernels: Learning Compact Hilbert Space Representations
Su Ann Low, Quentin Rommel, Kevin S. Miller +2
Function encoders are a recent technique that learn neural network basis functions to form compact, adaptive representations of Hilbert spaces of functions. We show that function e…
Zero to Autonomy in Real-Time: Online Adaptation of Dynamics in Unstructured Environments
William Ward, Sarah Etter, Jesse Quattrociocchi +3
Autonomous robots must go from zero prior knowledge to safe control within seconds to operate in unstructured environments. Abrupt terrain changes, such as a sudden transition to i…
Online Adaptation of Terrain-Aware Dynamics for Planning in Unstructured Environments
William Ward, Sarah Etter, Tyler Ingebrand +3
Autonomous mobile robots operating in remote, unstructured environments must adapt to new, unpredictable terrains that can change rapidly during operation. In such scenarios, a cri…
Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces
Tyler Ingebrand, Adam J. Thorpe, Ufuk Topcu
A central challenge in transfer learning is designing algorithms that can quickly adapt and generalize to new tasks without retraining. Yet, the conditions of when and how algorith…