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..
Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting
Authors: Xiajun Jiang, Ryan Missel, Zhiyuan Li, Linwei Wang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compared the presented framework with a comprehensive set of baseline models 1) trained globally on the large meta-training set with diverse dynamics, 2) trained individually on single dynamics with and without fine-tuning to k-shot support series, and 3) extended to few-shot meta-formulations. We demonstrated that the presented framework is agnostic to the latent dynamic function of choice and, at meta-test time, is able to forecast for new dynamics given variable-shot of support series. |
| Researcher Affiliation | Academia | Golisano College of Computing and Information Sciences Rochester Institute of Technology Rochester, NY 14623, USA EMAIL |
| Pseudocode | No | The paper describes the methodology in prose and mathematical equations but does not include any explicit pseudocode blocks or algorithm figures. |
| Open Source Code | Yes | Source code available at https://github.com/john-x-jiang/meta_ssm. |
| Open Datasets | Yes | Data: We first considered benchmark images generated with controllable physics, including bouncing ball Fraccaro et al. (2017), Hamiltonian pendulum (Botev et al., 2021), and Hamiltonian mass-spring systems (Botev et al., 2021). Details of data generation are available in Appendix G... All ball data can be found here: https://drive.google.com/drive/ folders/1Tm3DNrugc Sb WXSNye GL3j QKR8y3i Xx0m?usp=sharing. The heart data can be found here: https://drive.google.com/drive/folders/ 12S579V0KWMgb HGXDQZt0r Qyfz F1Ay NCu?usp=sharing. |
| Dataset Splits | Yes | For gravity-16 data, we used 10 gravity in meta-training, 2 in meta-validation, and 4 in meta-testing. |
| Hardware Specification | Yes | All experiments were run on NVIDIA Tesla T4s with 16 GB memory. |
| Software Dependencies | No | The paper specifies the optimizer (Adam) and neural network architectures (e.g., GRU-res, NODE, RGN-res) but does not provide specific version numbers for software dependencies like Python, PyTorch, TensorFlow, or CUDA. |
| Experiment Setup | Yes | Detailed hyperparameter settings are shown below. Meta Model Architecture on Mixed-Physics and Gravity-16: Domain Input: 20 observation timesteps of 32 32 dimensions, Initialization Input: 3 observation timesteps of 32 32 dimensions, Optimizer: Adam, 5 10 4 learning rate, Batch size: 50, Number of epochs: 200, Latent Units: 8, Transition Units: 100, Domain Encoder Filters: [8, 16, 8], Domain Time Units: [10, 5, 1], Initial Encoder Filters: [8, 16, 8], Emission Filters: [32, 16, 8, 1], KL term initialization: λ1 = 10 2, KL term set-embedding: λ2 = 10 2. |