not-MIWAE: Deep Generative Modelling with Missing not at Random Data
Authors: Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we apply the not-MIWAE to problems with values MNAR: censoring in multivariate datasets, clipping in images and selection bias in recommender systems. Implementation details and a link to source code can be found in appendix A. |
| Researcher Affiliation | Academia | Niels Bruun Ipsen nbip@dtu.dk Pierre-Alexandre Mattei pierre-alexandre.mattei@inria.fr Jes Frellsen jefr@dtu.dk Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark Universit e Cˆote d Azur, Inria (Maasai team), Laboratoire J.A. Dieudonn e, UMR CNRS 7351, France |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code is available at: https://github.com/nbip/not MIWAE |
| Open Datasets | Yes | We compare different imputation techniques on datasets from the UCI database (Dua & Graff, 2017), street view house numbers dataset (SVHN, Netzer et al., 2011) and The Yahoo! R3 dataset (webscope.sandbox.yahoo.com). |
| Dataset Splits | No | While the Yahoo! R3 dataset describes separate training and test sets, the paper does not provide specific percentages, counts, or a methodology for data splitting (including validation splits) for any of the datasets (UCI, SVHN, Yahoo! R3) that would be needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only general software frameworks. |
| Software Dependencies | No | The paper mentions using 'TensorFlow probability (Dillon et al., 2017) and the Adam optimizer (Kingma & Ba, 2014)' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The encoder and decoder consist of two hidden layers with 128 units and tanh activation functions. ... The size of the latent space is set to p − 1, K = 20 importance samples were used during training and a batch size of 16 was used for 100k iterations. ... K = 5 importance samples were used during training and a batch size of 64 was used for 1M iterations. ... We use K = 20 importance samples during training, ReLU activations, a batch size of 100 and train for 10k iterations. |