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Found 6 papers, 0 papers with code
Adaptive Transfer Learning: a simple but effective transfer learning
Our empirical evaluations suggest that ATL can help DL models learn more efficiently, especially when available examples are limited.
Deep Learning Explicit Differentiable Predictive Control Laws for Buildings
We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems.
One Representation to Rule Them All: Identifying Out-of-Support Examples in Few-shot Learning with Generic Representations
We describe how this problem is subtly different from out-of-distribution detection and describe a new method of identifying OOS examples within the Prototypical Networks framework using a fixed point which we call the generic representation.
Prototypical Region Proposal Networks for Few-Shot Localization and Classification
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.
Constrained Block Nonlinear Neural Dynamical Models
Neural network modules conditioned by known priors can be effectively trained and combined to represent systems with nonlinear dynamics.
Physics-Informed Neural State Space Models via Learning and Evolution
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.
