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..
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
Authors: Shikai Fang, Xin Yu, Zheng Wang, Shibo Li, Mike Kirby, Shandian Zhe
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The advantage of our method is shown in both synthetic data and several realworld applications. We release the code of Fun Ba T at https://github.com/ xuangu-fang/Functional-Bayesian-Tucker-Decomposition |
| Researcher Affiliation | Academia | University of Utah, Salt Lake City, UT 84112, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 Fun Ba T |
| Open Source Code | Yes | We release the code of Fun Ba T at https://github.com/ xuangu-fang/Functional-Bayesian-Tucker-Decomposition |
| Open Datasets | Yes | Datasets We evaluated Fun Ba T on four real-world datasets: Beijing Air-PM2.5, Beijing Air-PM10, Beijing Air-SO2 and US-TEMP. The first three are extracted from Beijing Air1... We obtain US-TEMP from the Climate Change2. 1https://archive.ics.uci.edu/ml/datasets/Beijing+Multi-Site+ Air-Quality+Data 2https://berkeleyearth.org/data/ |
| Dataset Splits | Yes | Following (Tillinghast et al., 2020), we randomly sampled 80% observed entry values for training and then tested on the remaining. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU, CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions "We used Py Torch to implement Fun Ba T", but it does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We re-scaled all continuous-mode indexes to [0, 1] to ensure numerical robustness. For Fun Ba T, we varied Matérn kernels ν = {1/2, 3/2} along the kernel parameters for optimal performance for different datasets. We examined all the methods with rank R {2, 3, 5, 7}. We set all modes ranks to R. |