Stochastic Nonparametric Event-Tensor Decomposition
Authors: Shandian Zhe, Yishuai Du
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Evaluations on both synthetic and real-world datasets show that our model not only improves upon the predictive performance of existing methods, but also discovers interesting clusters underlying the data. |
| Researcher Affiliation | Academia | Shandian Zhe, Yishuai Du School of Computing, University of Utah zhe@cs.utah.edu, yishuai.du@utah.edu |
| Pseudocode | No | The paper describes the 'Doubly Stochastic Variational Expectation-Maximization Inference' algorithm in Section 4.2 textually but does not provide a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not contain any explicit statement about the release of its source code, nor does it provide a link to a code repository or mention code in supplementary materials. |
| Open Datasets | Yes | To examine the predictive performance, we used three real-world datasets, Article(www. kaggle.com/gspmoreira/articles-sharing-reading-from-cit-deskdrop/ data), UFO(www.kaggle.com/NUFORC/ufo-sightings/data) and 911(www.kaggle. com/mchirico/montcoalert/data). |
| Dataset Splits | Yes | For training, we used the first 50K, 40K and 40K events in Article, UFO and 911 respectively, and the remaining 22.3K, 19.3K and 30.4K events for testing. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments, such as CPU or GPU models, memory, or cloud computing instances. |
| Software Dependencies | No | The paper mentions using 'Ada Delta' for adjusting step-size but does not provide specific software dependencies like programming languages, libraries, or frameworks with version numbers (e.g., Python, PyTorch, TensorFlow versions) that would be needed for replication. |
| Experiment Setup | Yes | We varied the number of latent factors from {1, 2, 5, 8}. For both GP-PTF and our method, we used the ARD kernel and set the number of pseudo inputs to 100... The mini-batch sizes of tensor entries (for all the methods), and events (for our method only) are both set to 100. We used Ada Delta (Zeiler, 2012) to adjust the step-size for the stochastic gradient ascent, and ran 100 epochs for each method. |