Robust and Scalable SDE Learning: A Functional Perspective
Authors: Scott Alexander Cameron, Tyron Luke Cameron, Arnu Pretorius, Stephen J. Roberts
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide some experiments to illustrate and verify the performance and capabilities of our proposed algorithm. |
| Researcher Affiliation | Collaboration | Scott Cameron Oxford University, Insta Deep Ltd. United Kingdom s.cameron@instadeep.com Tyron Cameron Discovery Insure South Africa Arnu Pretorius Insta Deep Ltd. South Africa Stephen Roberts Oxford University United Kingdom |
| Pseudocode | Yes | Algorithm 1 Path Integral Importance Sampling; Algorithm 2 Transformed-State Path Integral Importance Sampling |
| Open Source Code | Yes | The code included in the supplementary material includes a Dockerfile and instructions for running some of the experiments. |
| Open Datasets | Yes | The data set we used was the Hungarian chickenpox cases dataset from the UCI machine learning repository; it has 20 features per observation. This dataset can be found at https://archive.ics.uci.edu/ml/datasets/Hungarian+Chickenpox+Cases |
| Dataset Splits | No | The paper uses '16 independent paths' and 'observations' for training and evaluation metrics, but it does not specify explicit train/validation/test dataset splits (e.g., percentages or counts for distinct sets). |
| Hardware Specification | Yes | These models were trained on an Nvidia RTX 3070, with 8Gb of memory. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' and 'neural network' architectures, but does not provide specific version numbers for software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages. |
| Experiment Setup | Yes | For each model we used the Adam optimizer with a learning rate of 10-3 and ran the optimization algorithm for 104 iterations. In both cases we used K = 64 and a time step size of t = 10-2. |