Bayesian Continuous-Time Tucker Decomposition
Authors: Shikai Fang, Akil Narayan, Robert Kirby, Shandian Zhe
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For evaluation, we examined our approach in both ablation study and real-world applications. On synthetic datasets, BCTT successful learned different temporal dynamics and recovered the clustering structures of the tensor nodes from their factor estimation. On three real-world temporal tensor datasets, BCTT significantly outperforms the competing dynamic decomposition methods, including discrete time factors and continuous time coefficients, often by a large margin. |
| Researcher Affiliation | Academia | 1School of Computing, University of Utah 2 Scientific Computing and Imaging (SCI) Institute, University of Utah 3Department of Mathematics, University of Utah. Correspondence to: Shandian Zhe <zhe@cs.utah.edu>. |
| Pseudocode | Yes | Algorithm 1 BCTT |
| Open Source Code | No | The paper mentions implementing our method BCTT with PyTorch (Paszke et al., 2019) but does not provide a link or explicit statement about releasing the source code for their method. |
| Open Datasets | Yes | (1) Movie Len100K (https://grouplens.org/datasets/movielens/)... (2) Ads Click (https://www.kaggle.com/ c/avazu-ctr-prediction)... (3) DBLP (https://dblp.uni-trier.de/ xml/)... |
| Dataset Splits | Yes | we randomly sampled 80% observed entry values and their time points for training, and then tested on the remaining entries. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or cloud instance specifications used for running experiments. |
| Software Dependencies | No | The paper mentions 'PyTorch' but does not specify its version number or any other software dependencies with explicit version numbers. |
| Experiment Setup | Yes | We used the Mat ern kernel with ν = 3/2, and set l = σ2 = 0.1. We ran our message-passing inference until convergence. The tolerance level was set to 10 3. ... For CT-CP, we used 100 knots for the polynomial splines. Except BCTT, all the methods were trained with stochastic mini-batch optimization, with mini-batch size 100. We used ADAM optimization (Kingma and Ba, 2014). The learning rate was chosen from {10 4, 5 10 4, 10 3, 5 10 3, 10 2}. |