AI RESEARCH
Time-Warping Recurrent Neural Networks for Transfer Learning
arXiv CS.LG
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ArXi:2604.02474v1 Announce Type: new 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.