Scalable Bayesian Low-Rank Decomposition of Incomplete Multiway Tensors
Authors: Piyush Rai, Yingjian Wang, Shengbo Guo, Gary Chen, David Dunson, Lawrence Carin
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments with our model on both synthetic and real-world tensor datasets, and compare it with several baselines. The datasets used in our experiments span a wide range of application domains, such as chemometrics, multirelational social networks, brain-signal analysis (EEG), and image analysis. |
| Researcher Affiliation | Collaboration | Piyush Rai PIYUSH.RAI@DUKE.EDU Yingjian Wang YW65@DUKE.EDU Shengbo Guo S.GUO@SAMSUNG.COM Gary Chen GARY.CHEN@SAMSUNG.COM David Dunson DUNSON@DUKE.EDU Lawrence Carin LCARIN@DUKE.EDU Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA Samsung Research America Department of Statistical Science, Duke University, Durham, NC 27708, USA |
| Pseudocode | No | The paper describes the Gibbs sampling updates mathematically but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | Amino Acid data (Xu et al., 2013; Chu & Ghahramani, 2009), Flow Injection data (Xu et al., 2013; Chu & Ghahramani, 2009), Lazega-Lawyers multirelational social network data (Lazega, 2001), Kinship multirelational data (Nickel et al., 2011), Nation multirelational data (Nickel et al., 2011), benchmark Lena image. |
| Dataset Splits | Yes | For this task, we treat 50% of the data as missing and reconstruct it using the model learned on the remaining 50% data.For each dataset, except Kinship, we treat 90% of the entries as missing and predict them using the rest 10% data. For Kinship data we use the experimental setting of 90% training and 10% test data as done in other recent works (Nickel et al., 2011; Jenatton et al., 2012).Each experiment is repeated 10 times with different splits of observed and missing data. |
| Hardware Specification | No | The paper states it used an 'unoptimized MATLAB implementation' but does not provide any specific hardware details such as CPU, GPU, or memory specifications for running the experiments. |
| Software Dependencies | No | The paper mentions using an 'unoptimized MATLAB implementation' but does not specify any software versions or other dependencies. |
| Experiment Setup | Yes | For our model, we set µ(k) r to zero vector and Σ(k) r to identity matrix.We run the sampling based methods for 1500 iterations with 1000 burn-in iterations, collect samples every 5 iterations after the burnin phase, and report all results using the posterior sample based averages. |