Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Self-Modulating Nonparametric Event-Tensor Factorization
Authors: Zheng Wang, Xinqi Chu, Shandian Zhe
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 19,355 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,780 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For evaluation, we examined our method on three real-world datasets. Our model nearly always achieves better predictive performance than the existing methods using Poisson processes, time factors, and Hawkes processes to incorporate temporal information. |
| Researcher Affiliation | Collaboration | 1School of Computing, University of Utah 2Xjera Labs, Pte. Ltd. |
| Pseudocode | No | While there is an 'Algorithm' section, it describes the algorithm verbally and mathematically rather than providing a structured pseudocode block. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of its source code. |
| Open Datasets | Yes | Taobao (https://tianchi.aliyun.com/dataset/ data Detail?data Id=53), UFO (https://www.kaggle.com/ NUFORC/ufo-sightings/data), Crash (https://www.kaggle.com/ usdot/nhtsa-traffic-fatalities) |
| Dataset Splits | No | For training, we used the ο¬rst 40K, 40K, 20K events from Taobao, UFO and Crash, respectively. The remaining 29.8K, 30.4K, 12K events were used for test. The paper specifies training and test sets but does not mention a validation split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | We implemented our approach and HP-Local with Py Torch and used ADAM (Kingma and Ba, 2014) for stochastic optimization. While software names are mentioned, no version numbers are provided. |
| Experiment Setup | Yes | For both methods, the mini-batch size was set to 100. The learning rate was chosen from {5 10 4, 10 3, 3 10 3, 5 10 3, 10 2}. We ran each method for 400 epochs |