Compressing Neural Networks Using Tensor Networks with Exponentially Fewer Variational Parameters
The neural network (NN) designed for challenging machine learning tasks is in general a highly nonlinear mapping that contains numerous variational parameters. The complexity of NNs, if unbounded or unconstrained, might unpredictably cause severe issues including overfitting, loss of generalization...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
American Association for the Advancement of Science (AAAS)
2025-01-01
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| Series: | Intelligent Computing |
| Online Access: | https://spj.science.org/doi/10.34133/icomputing.0123 |
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