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Found 5 papers, 2 papers with code
Discovering More Effective Tensor Network Structure Search Algorithms via Large Language Models (LLMs)
Tensor network structure search (TN-SS), aiming at searching for suitable tensor network (TN) structures in representing high-dimensional problems, largely promotes the efficacy of TN in various machine learning applications.
SVDinsTN: A Tensor Network Paradigm for Efficient Structure Search from Regularized Modeling Perspective
To address this issue, we propose a novel TN paradigm, named SVD-inspired TN decomposition (SVDinsTN), which allows us to efficiently solve the TN-SS problem from a regularized modeling perspective, eliminating the repeated structure evaluations.
Alternating Local Enumeration (TnALE): Solving Tensor Network Structure Search with Fewer Evaluations
Tensor network (TN) is a powerful framework in machine learning, but selecting a good TN model, known as TN structure search (TN-SS), is a challenging and computationally intensive task.
Permutation Search of Tensor Network Structures via Local Sampling
Recent works put much effort into tensor network structure search (TN-SS), aiming to select suitable tensor network (TN) structures, involving the TN-ranks, formats, and so on, for the decomposition or learning tasks.
Graph-Constrained Structure Search for Tensor Network Representation
Recent works paid effort on the structure search issue for tensor network (TN) representation, of which the aim is to select the optimal network for TN contraction to fit a tensor.
