A pivot-model abstraction method enables automatic migration of neural network implementations between frameworks such as PyTorch and TensorFlow while preserving functional equivalence.
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Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
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Towards Migrating Neural Network Implementations
A pivot-model abstraction method enables automatic migration of neural network implementations between frameworks such as PyTorch and TensorFlow while preserving functional equivalence.
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Quantum-inspired tensor networks in machine learning models
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.