Introduces two algorithms for efficient finite initialization of tensor network layers via iterative partial norm computations, applied to MPS/TT and MPO/TT-M layers with scaling analysis and public code.
(a) If it is infinite or zero, we divide each element of the nodes of A by (pL||A||n−1,N) 1 N , and re- peat Steps 2 and 4 (from this value of n)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2023 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Efficient Finite Initialization with Partial Norms for Tensorized Neural Networks and Tensor Networks Algorithms
Introduces two algorithms for efficient finite initialization of tensor network layers via iterative partial norm computations, applied to MPS/TT and MPO/TT-M layers with scaling analysis and public code.