Notice: 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. Full accuracy metrics and methodology are described in [1].
Meta Temporal Point Processes
Authors: Wonho Bae, Mohamed Osama Ahmed, Frederick Tung, Gabriel L. Oliveira
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 EXPERIMENTS |
| Researcher Affiliation | Collaboration | Wonho Bae University of British Columbia & Borealis AI EMAIL Mohamed Osama Ahmed Borealis AI EMAIL Frederick Tung Borealis AI EMAIL Gabriel L. Oliveira Borealis AI EMAIL |
| Pseudocode | No | The paper describes the architecture and steps of the proposed methods in text and uses block diagrams, but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our implementation is publicly available at https://github.com/Borealis AI/meta-tpp. |
| Open Datasets | Yes | To compare the effectiveness of models, we conduct experiments on 4 popular benchmark datasets Stack Overflow, Mooc, Reddit, and Wiki, and 3 datasets with strong periodic patterns we introduce Sinusoidal wave, Uber, and NYC Taxi. Please refer to Appendix H for details. |
| Dataset Splits | No | The paper does not explicitly provide the specific percentages or counts for training, validation, and test splits. It mentions 'training set' and 'test sets' but lacks detailed split methodology. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and that the code is based on PyTorch, but it does not specify version numbers for Python, PyTorch, or any other software dependencies. |
| Experiment Setup | Yes | Hyperparameters. We grid-search on every combination of dataset and method for learning rate {0.01, 0.001, 0.0001, 0.00001} and weight decay {0.01, 0.001, 0.0001, 0.00001} for fair comparison. ... All the other hyperparameters are fixed throughout the experiments, and are reported in Appendix I. |