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..
Neural Jump Stochastic Differential Equations
Authors: Junteng Jia, Austin R. Benson
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the predictive capabilities of our model on a range of synthetic and real-world marked point process datasets, including classical point processes (such as Hawkes processes), awards on Stack Overflow, medical records, and earthquake monitoring. |
| Researcher Affiliation | Academia | Junteng Jia Cornell University EMAIL Austin R. Benson Cornell University EMAIL |
| Pseudocode | Yes | The complete algorithm for simulating the hybrid system with stochastic events is described in Appendix A.1. |
| Open Source Code | Yes | The complete implementation of our algorithms and experiments are available at https://github.com/000Justin000/torchdiffeq/tree/jj585. |
| Open Datasets | Yes | We use our model to predict the time and locations of earthquakes above level 4.0 in 2007 2018 using historical data from 1970 2006. Data from https://www.kaggle.com/danielpe/earthquakes |
| Dataset Splits | Yes | For each generative process, we create a dataset by simulating 500 event sequences within the time interval [0, 100] and use 60% for training, 20% for validation and 20% for testing. |
| Hardware Specification | Yes | We train all of our models on a workstation with a 8 core i7-7700 CPU @ 3.60GHz processor and 32 GB memory. |
| Software Dependencies | No | The paper mentions using the "Adam optimizer" and refers to its implementation being available via a GitHub link that includes "torchdiffeq", but it does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We use the Adam optimizer with β1 = 0.9, β2 = 0.999; the architectures, hyperparameters, and learning rates for different experiments are reported below. ... the learning rate for the Adam optimizer is set to be 10 3 with weighted decay rate 10 5. |