Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes
Authors: Zheng Wang, Shikai Fang, Shibo Li, Shandian Zhe
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the advantage of our approach in both simulation study and real-world applications. |
| Researcher Affiliation | Academia | Zheng Wang Kahlert School of Computing University of Utah Salt Lake City, UT 84112 u1208847@utah.edu Shikai Fang Kahlert School of Computing University of Utah Salt Lake City, UT 84112 shikai.fang@utah.edu Shibo Li Kahlert School of Computing University of Utah Salt Lake City, UT 84112 shibo@cs.utah.edu Shandian Zhe Kahlert School of Computing University of Utah Salt Lake City, UT 84112 zhe@cs.utah.edu |
| Pseudocode | No | The paper describes various algorithmic steps and formulations but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/wzhut/Dynamic-Tensor-Decompositionvia-Neural-Diffusion-Reaction-Processes. |
| Open Datasets | Yes | Datasets. We next evaluated the predictive performance of DEMOTE in three real-world applications. (1) CA Weather (Moosavi et al., 2019) (https://smoosavi.org/datasets/lstw)... (2) CA Traffic (Moosavi et al., 2019) (https://smoosavi.org/dataset s/lstw)... (3) Server Room (https://zenodo.org/record/3610078#.Xl Np Aigza M8)... |
| Dataset Splits | No | The paper specifies a train/test split (e.g., 'randomly draw 80% observed entries and their time stamps for training, with the remaining for test') but does not explicitly mention a separate validation split or cross-validation for its own method. |
| Hardware Specification | Yes | We tested all the methods in a workstation with one NVIDIA Ge Force RTX 3090 Graphics Card, 10th Generation Intel Core i910850K Processor, 32 GB RAM, and 1 TB SSD. |
| Software Dependencies | No | The paper mentions 'Pytorch' and 'torchdiffeq library' but does not specify their version numbers for reproducibility. |
| Experiment Setup | Yes | We set the mini-batch size to 50, and used ADAM (Kingma and Ba, 2014) algorithm for stochastic optimization. The learning rate was automatically adjusted in [10 4, 10 1] by the Reduce LROn Plateau scheduler (Al-Kababji et al., 2022). The maximum number of epochs is 2K, which is enough for convergence. |