Practical Conditional Neural Process Via Tractable Dependent Predictions
Authors: Stratis Markou, James Requeima, Wessel Bruinsma, Anna Vaughan, Richard E Turner
ICLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the proposed models to synthetic and real data. Our experiments with synthetic data comprise four Gaussian tasks and a non-Gaussian task. In our experiments with real data, we evaluate our models on electroencephalogram data as well as climate data. |
| Researcher Affiliation | Academia | Stratis Markou University of Cambridge EMAIL James Requeima University of Cambridge Invenia Labs EMAIL Wessel P. Bruinsma University of Cambridge Invenia Labs EMAIL Anna Vaughan University of Cambridge EMAIL Richard E. Turner University of Cambridge EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | We publish the complete repository containing all our code, including the models, training scripts, pretrained models and Jupyter notebooks which will produce all plots in this paper (link3). https://github.com/censored/for-anonymity |
| Open Datasets | Yes | The EEG data is available via the UCI dataset website (link1). ... The climate modelling data is also available to the public, through the Copernicus European Climate Data Store (link2). |
| Dataset Splits | Yes | We sample 86 of the subjects for training, 10 for validation and 10 for testing. ... We train each of our meta-models for 1000 epochs, each consisting of 256 iterations, at each of which the model is presented with a batch 16 different tasks. |
| Hardware Specification | Yes | Table 3 shows the runtime and memory footprint of the GNP, AGNP, Conv GNP and Full Conv GNP models applied to data with one-dimensional inputs, D = 1. In particular, we measure the runtime and memory required to perform a single forward pass through the neural architecture of each model that was used for the one-dimensional Gaussian tasks, on an NVIDIA Ge Force RTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions software like Adam optimizer, scipy's L-BFGS-B, and a specific Git Hub implementation (link5) but does not provide specific version numbers for these or other key software components used in the experiments. |
| Experiment Setup | Yes | We optimise all models with Adam (Kingma & Ba, 2014), using a learning rate of 5 10 4. ... For the Conv NP we use 10 latent samples to evaluate the loss during training, and 512 samples during testing. ... Each model is trained for 500 epochs. |