Permutation Search of Tensor Network Structures via Local Sampling
Authors: Chao Li, Junhua Zeng, Zerui Tao, Qibin Zhao
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct a theoretical investigation of TN-PS and propose a practically-efficient algorithm to resolve the problem. Theoretically, we prove the counting and metric properties of search spaces of TN-PS... Numerically, we propose a novel meta-heuristic algorithm... Numerical results demonstrate that the new algorithm can reduce the required model size of TNs in extensive benchmarks... |
| Researcher Affiliation | Academia | 1 RIKEN Center for Advanced Intelligence Project (RIKEN-AIP), Tokyo, Japan 2 School of Automation, Guangdong University of Technology, Guangzhou, China 3 Tokyo University of Agriculture and Technology, Tokyo, Japan. |
| Pseudocode | Yes | Algorithm 1 Random sampling over Id(G)... Algorithm 2 TN-structure Local Sampling (TNLS) |
| Open Source Code | Yes | Our code is available at https://github.com/Chao Li At RIKEN/TNLS. |
| Open Datasets | Yes | The Combined Cycle Power Plant (CCPP) (Tufekci, 2014) dataset... The MG (Flake & Lawrence, 2002) data... The Protein (Dua & Graff, 2017) data... we randomly select 10 natural images from the BSD500 (Arbelaez et al., 2010). |
| Dataset Splits | No | For all the datasets, we randomly choose 80% of the data for training and the rest for testing, then standardize the training and testing sets respectively by removing the mean and scaling to unit variance... No explicit mention of a separate validation split or its size. |
| Hardware Specification | No | Part of the computation was carried out at the Riken AIp Deep learning ENvironment (RAIDEN). This refers to a computing environment but does not provide specific hardware details like GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer (Kingma & Ba, 2014)' and 'Matlab commands resize and rgb2gray' but does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | In our method, we set the template G0 as a cycle graph, the searching range for TN-ranks R = 7, the maximum iteration #Iter = 30, the number of samples #Sample = 60, and the tuning parameters c1 = 0.9 and c2 = 0.9, 0.94, 0.98... We set the learning rate of the Adam optimizer (Kingma & Ba, 2014) to 0.001. The maximum number of generations is set to be 30. The population in each generation is set to be 150... elimination rate is 36%... There is a chance of 24% for each gene to mutate... |