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..
Multi-Mode Deep Matrix and Tensor Factorization
Authors: Jicong Fan
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on synthetic data and real datasets showed that the proposed methods have much higher recovery accuracy than many baselines. |
| Researcher Affiliation | Academia | Jicong Fan1,2 1School of Data Science, The Chinese University of Hong Kong (Shenzhen), China 2Shenzhen Research Institute of Big Data, China |
| Pseudocode | Yes | Algorithm 1 Gradient-based optimization for M2DMTF (12) |
| Open Source Code | Yes | Codes link: https://github.com/jicongfan/Multi-Mode-Deep-Matrix-and-Tensor-Factorization |
| Open Datasets | Yes | We consider two benchmark datasets: Movie Lens-100k and Movie Lens-1M... We compare the proposed method M2DMTF with the baselines on the following datasets: Amino acid fluorescence (Bro, 1997) (5 201 61), Flow injection (Nørgaard & Ridder, 1994) (12 100 89), and SW-NIR kinetic data (Bijlsma & Smilde, 2000) (301 241 8). |
| Dataset Splits | No | The paper mentions 'determine the hyper parameters of all methods via cross-validation' but does not explicitly state a separate validation dataset split with specific percentages or sample counts for the reported results in tables. The tables only show 'Train/Test' ratios. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments were provided. |
| Software Dependencies | No | The paper mentions using MATLAB and Python, and refers to optimizers like 'i Rprop+' and 'Adam', but does not provide specific version numbers for any software or libraries. |
| Experiment Setup | Yes | In MF (problem (1) in the main paper), the factorization dimension d is 5 because it outperforms other choices. The λ is chosen from {0.01, 0.1, 1} and the optimizer is i Rprop+. The maximum iteration is 2000. ... In M2DMTF, L = 2, d1 = d2 = 3, h(1) 1 = h(2) 1 = 10, m1 = m2 = 20, and λ1 = λ 2 = 1. ... The activation function is the hyperbolic tangent function. The optimizer is i Rprop+ and the maximum iteration is 3000. |