Tensor Completion Made Practical
Authors: Allen Liu, Ankur Moitra
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we describe our experimental results and in particular how our algorithm compares to existing algorithms. |
| Researcher Affiliation | Academia | Allen Liu Massachusetts Institute of Technology Cambridge, MA 02139 cliu568@mit.edu Ankur Moitra Department of Mathematics, Massachusetts Institute of Technology Cambridge, MA 02139 moitra@mit.edu |
| Pseudocode | Yes | Algorithm 1 FULL EXACT TENSOR COMPLETION |
| Open Source Code | Yes | The code for all of the experiments can be found in Section I. |
| Open Datasets | No | Uncorrelated tensors: generated by taking T = P4 i=1 xi yi zi where xi, yi, zi are random unit vectors. Correlated tensors: generated by taking T = P4 i=1 0.5i 1xi yi zi where x1, y1, z1 are random unit vectors and for i > 1, xi, yi, zi are random unit vectors that have covariance 0.88 with x1, y1, z1 respectively. |
| Dataset Splits | No | The paper mentions using a random subset of observations for alternating minimization steps but does not provide explicit train/validation/test dataset splits, percentages, or absolute counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory, or computational resources used for running the experiments. |
| Software Dependencies | No | The paper does not specify software dependencies with version numbers, such as programming languages, libraries, or frameworks used for the implementation. |
| Experiment Setup | Yes | We ran KRONECKER COMPLETION and STANDARD ALTERNATING MINIMIZATION for n = 200, r = 4 and either 50000 or 200000 observations. We ran 100 trials and took the median normalized MSE... For alternating minimization steps, we use a random subset consisting of half of the observations. We ran KRONECKER COMPLETION for 100 iterations compared to running STANDARD ALTERNATING MINIMIZATION for 400 iterations |