Practical Conditional Neural Process Via Tractable Dependent Predictions
Authors: Stratis Markou, James Requeima, Wessel Bruinsma, Anna Vaughan, Richard E Turner
ICLR 2022 | Conference 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 em626@cam.ac.uk James Requeima University of Cambridge Invenia Labs jrr41@cam.ac.uk Wessel P. Bruinsma University of Cambridge Invenia Labs wpb23@cam.ac.uk Anna Vaughan University of Cambridge av555@cam.ac.uk Richard E. Turner University of Cambridge ret26@cam.ac.uk |
| 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. |