Tensor Decomposition with Smoothness
Authors: Masaaki Imaizumi, Kohei Hayashi
ICML 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The theoretical result and performances of STD are numerically verified. (Abstract); These results are empirically confirmed through experiments using synthetic and real data. (Introduction); 5. Experiment (Section title) |
| Researcher Affiliation | Academia | 1Institute of Statistical Mathematics 2National Institute of Advanced Industrial Science and Technology 3RIKEN. |
| Pseudocode | No | The paper describes the ADMM algorithm steps in section 3.3 using equations and prose, but it does not provide a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, such as a specific repository link or an explicit code release statement. |
| Open Datasets | Yes | We conduct tensor completion and interpolation using amino acids data (Kiers, 1998).; We conduct an experiment with a human activity video dataset (Schuldt et al., 2004). |
| Dataset Splits | No | The paper mentions hyperparameters are determined through cross-validation, but it does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning into train/validation/test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | λn and µn can be determined through cross validation. {M (k)} is initialized as a large value and reduced during the algorithm depending on µn. (Section 3.4); The scale of noise is varied as σ ∈ {0.01, 0.1}. (Section 5.1); For STD, we set the basis functions as trigonometric series and λn = µn = 0.1. (Section 5.3.2) |