Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
Authors: Shikai Fang, Xin Yu, Zheng Wang, Shibo Li, Mike Kirby, Shandian Zhe
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | 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 {shikai, xiny, wzuht, shibo, kirby, zhe}@cs.utah.edu |
| 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. |