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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |