Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Tensor Decomposition with Smoothness
Authors: Masaaki Imaizumi, Kohei Hayashi
ICML 2017 | Venue PDF | 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) |