Supervised Learning with Tensor Networks
Authors: Edwin Stoudenmire, David J. Schwab
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For the MNIST data set we obtain less than 1% test set classification error. |
| Researcher Affiliation | Academia | E. M. Stoudenmire Perimeter Institute for Theoretical Physics Waterloo, Ontario, N2L 2Y5, Canada David J. Schwab Department of Physics Northwestern University, Evanston, IL |
| Pseudocode | No | The paper describes algorithms through text and diagrams (e.g., Fig. 6, Fig. 7) but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | For researchers interested in reproducing our results, we have made our codes publicly available at: https://github.com/emstoudenmire/TNML. The codes are based on the ITensor library [19]. |
| Open Datasets | Yes | To test the tensor network approach on a realistic task, we used the MNIST data set [24]. |
| Dataset Splits | No | The paper mentions 'training or test sets' and 'training set error' and 'test error' but does not specify a validation set or explicit dataset splits (e.g., percentages, sample counts, or predefined splits for train/validation/test). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper states 'The codes are based on the ITensor library [19]' but does not provide specific version numbers for ITensor or other software dependencies. |
| Experiment Setup | Yes | Each image was scaled down from 28x28 to 14x14 by averaging clusters of four pixels... We chose a zig-zag ordering meaning the first row of pixels are mapped to the first 14 external MPS indices... Using the sweeping algorithm in Section 4 to optimize the weights, we found the algorithm quickly converged after a few passes, or sweeps, over the MPS. Typically five or less sweeps were needed... Test error rates also decreased rapidly with the maximum MPS bond dimension m. For m = 10... for m = 20... The largest bond dimension we tried was m = 120... |