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Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Network. (arXiv:2210.14064v1 [cs.LG])
Oct. 26, 2022, 1:12 a.m. | Edo Cohen-Karlik, Itamar Menuhin-Gruman, Nadav Cohen, Raja Giryes, Amir Globerson
cs.LG updates on arXiv.org arxiv.org
Overparameterization in deep learning typically refers to settings where a
trained Neural Network (NN) has representational capacity to fit the training
data in many ways, some of which generalize well, while others do not. In the
case of Recurrent Neural Networks (RNNs), there exists an additional layer of
overparameterization, in the sense that a model may exhibit many solutions that
generalize well for sequence lengths seen in training, some of which
extrapolate to longer sequences, while others do not. Numerous …
arxiv low network neural network recurrent neural network state
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