Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
On the Generalization and Approximation Capacities of Neural Controlled Differential Equations
Authors: Linus Bleistein, Agathe Guilloux
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 32,784 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,260 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are validated through a series of experiments. Our theoretical results are illustrated by experiments on synthetic data. |
| Researcher Affiliation | Academia | Linus Bleistein Inria Paris, UEVE Agathe Guilloux Inria Paris |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at this link. |
| Open Datasets | No | The paper describes generating synthetic data using 'Python package stochastic' and 'f BM with Hurst parameter H'. It does not provide a specific link, DOI, repository name, or formal citation for a pre-existing, publicly available dataset. |
| Dataset Splits | No | The paper mentions training sample sizes and test sample sizes but does not specify a validation split or its size: 'The size of the training sample is set to n = 100.' and 'the expected generalization error is computed on 50 test samples.' |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for experiments. |
| Software Dependencies | No | The paper mentions 'Python package stochastic' and 'Pytorch s default initialization' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The model is initialized with Pytorch s default initialization. In the first and second figures (starting from the left), the model is trained for 2000 iterations with Adam. We use the default values for α, β and a learning rate of 5 × 10−3. The size of the training sample is set to n = 100. We train a shallow NCDE classifier with p = 3 on n = 100 time series sampled at 100 equidistant time points in [0, 1] for 100 iterations with Binary Cross Entropy (BCE) loss. We use Adam with default settings and a learning rate of 5 × 10−2. |