Time-Warping Recurrent Neural Networks for Transfer Learning
Dynamical systems describe how a physical system evolves over time. Physical processes can evolve faster or slower in different environmental conditions. We use time-warping as rescaling the time in a model of a physical system. This thesis proposes a new method of transfer learning for Recurrent Neural Networks (RNNs) based on time-warping. We prove that for a class of linear, first-order differential equations known as time lag models, an LSTM can approximate these systems with any desired accuracy, and the model can be time-warped while maintaining the approximation accuracy. The Time-Warping method of transfer learning is then evaluated in an applied problem on predicting fuel moistur...
| Authors | Jonathon Hirschi |
|---|---|
| Primary category | cs.LG |
| Published | 2 April 2026 |
| Last updated at source | 8 May 2026 |
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