Meta Temporal Point Processes

Authors: Wonho Bae, Mohamed Osama Ahmed, Frederick Tung, Gabriel L. Oliveira

ICLR 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental 5 EXPERIMENTS
Researcher Affiliation Collaboration Wonho Bae University of British Columbia & Borealis AI whbae@cs.ubc.ca Mohamed Osama Ahmed Borealis AI mohamed.o.ahmed@borealisai.com Frederick Tung Borealis AI frederick.tung@borealisai.com Gabriel L. Oliveira Borealis AI gabriel.oliveira@borealisai.com
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.