Probabilistic Forecasting with Stochastic Interpolants and Föllmer Processes
Authors: Yifan Chen, Mark Goldstein, Mengjian Hua, Michael Samuel Albergo, Nicholas Matthew Boffi, Eric Vanden-Eijnden
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We highlight the utility of our approach on several complex, high-dimensional forecasting problems, including stochastically forced Navier Stokes and video prediction on the KTH and CLEVRER datasets. The code is available at https://github.com/interpolants/forecasting. |
| Researcher Affiliation | Academia | 1Courant Institute of Mathematical Sciences, New York University, New York, NY, USA. |
| Pseudocode | Yes | Algorithm 1 Training Algorithm 2 Sampling Algorithm 3 Video EM: (Euler-Marayuma in Latent Space). |
| Open Source Code | Yes | The code is available at https://github.com/interpolants/forecasting. |
| Open Datasets | Yes | including stochastically forced Navier Stokes and video prediction on the KTH (Schuldt et al., 2004) and CLEVRER datasets (Yi et al., 2019). |
| Dataset Splits | No | We split the data into 90% training data and 10% test data. We use a batch size of 100. In total we train 50 epochs. While a test split is mentioned, a specific validation split percentage or methodology is not provided. |
| Hardware Specification | Yes | The model is trained on a single Nvidia A100 GPU and it takes less than 1 day. |
| Software Dependencies | No | We use the jax-cfd package (Dresdner et al., 2022) for the mesh generation and domain discretization. |
| Experiment Setup | Yes | We train the network using a batch size of 104, default Adam W optimizer with base learning rate l = 10 3 and cosine scheduler that decreases in each epoch the learning rate eventually to 0 after 300 epochs. |