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 [1].
Neural ODE Processes
Authors: Alexander Norcliffe, Cristian Bodnar, Ben Day, Jacob Moss, Pietro Liò
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To test the proposed advantages of NDPs we carried out various experiments on time series data. For the low-dimensional experiments in Sections 4.1 and 4.2, we use an MLP architecture for the encoder and decoder. For the high-dimensional experiments in Section 4.3, we use a convolutional architecture for both. We train the models using RMSprop (Tieleman & Hinton, 2012) with learning rate 1 × 10−3. Additional model and task details can be found in Appendices F and G, respectively. |
| Researcher Affiliation | Academia | Alexander Norcliffe Department of Computer Science University College London London, United Kingdom EMAIL Cristian Bodnar , Ben Day , Jacob Moss & Pietro Li o Department of Computer Science University of Cambridge Cambridge, United Kingdom EMAIL |
| Pseudocode | Yes | Algorithm 1: Learning and Inference in Neural ODE Processes |
| Open Source Code | Yes | Our code and datasets are available at https://github.com/crisbodnar/ndp. |
| Open Datasets | Yes | Our code and datasets are available at https://github.com/crisbodnar/ndp. ... To generate the distribution over functions, we sample these parameters from a uniform distribution over their respective ranges. We use 490 time-series for training and evaluate on 10 separate test time-series. Each series contains 100 points. |
| Dataset Splits | Yes | Overall, we generate a dataset with 1, 000 training time-series, 100 validation time-series and 200 test time-series, each using disjoint combinations of different calligraphic styles and dynamics. |
| Hardware Specification | Yes | The experiments were run on an Nvidia Titan XP. |
| Software Dependencies | No | The paper mentions 'torchdiffeq library' and 'Pytorch' but does not provide specific version numbers for software dependencies. |
| Experiment Setup | Yes | We train the models using RMSprop (Tieleman & Hinton, 2012) with learning rate 1 × 10−3. Additional model and task details can be found in Appendices F and G, respectively. |