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..
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion
Authors: Marin Biloš, Kashif Rasul, Anderson Schneider, Yuriy Nevmyvaka, Stephan Günnemann
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5 we empirically show that our model outperforms the baselines on all tasks. 5. Experiments |
| Researcher Affiliation | Collaboration | 1Technical University of Munich, Germany 2Machine Learning Research, Morgan Stanley, United States. |
| Pseudocode | Yes | In Algorithms 1 and 2 we provide the pseudocode for training the model and sampling new data, for DSPD-GP model. Algorithm 1 Training (DSPD-GP diffusion) Algorithm 2 Sampling (DSPD-GP diffusion) |
| Open Source Code | Yes | https://github.com/morganstanley/MSML/tree/main/ papers/Stochastic_Process_Diffusion |
| Open Datasets | Yes | We test our model as defined in Section 4.1 and Figure 2 against Time Grad (Rasul et al., 2021b) on three established real-world datasets: Electricity, Exchange and Solar (Lai et al., 2018). Table 4. Multivariate dimension, domain, frequency, total training time steps, and prediction length properties of the training datasets used in the forecasting experiments. |
| Dataset Splits | No | The paper uses "training data" and "test set" but does not provide specific split percentages, sample counts, or explicit cross-validation methodology for their experiments. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory specifications, or cloud computing instance types used for the experiments. |
| Software Dependencies | No | The paper does not specify version numbers for any software dependencies, such as Python, PyTorch, or other libraries, that would be needed for replication. |
| Experiment Setup | No | The paper discusses aspects of model architecture and training process (e.g., Algorithms 1 and 2) but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or a dedicated section detailing the experimental setup with concrete training configurations in the main text. |