MERA tensor networks produce continuously varying effective scaling dimensions along the Z3 chiral clock critical line, starting from 3-state Potts values as the chiral parameter increases.
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Hierarchical nonlinear tensor networks generate dense, conv, and attention weights from few cores, yielding extreme per-layer compression with competitive CIFAR-10 accuracy on VGG-16.
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Scaling at Chiral Clock Criticality via Entanglement Renormalization
MERA tensor networks produce continuously varying effective scaling dimensions along the Z3 chiral clock critical line, starting from 3-state Potts values as the chiral parameter increases.
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Automatically Differentiable Nonlinear Tensor Networks (ADNTNs) for Exponential Parameter Compression of Deep Neural Networks
Hierarchical nonlinear tensor networks generate dense, conv, and attention weights from few cores, yielding extreme per-layer compression with competitive CIFAR-10 accuracy on VGG-16.