Tomographic Auto-Encoder: Unsupervised Bayesian Recovery of Corrupted Data
Authors: Francesco Tonolini, Pablo Garcia Moreno, Andreas Damianou, Roderick Murray-Smith
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our model in a data recovery task under the common setting of missing values and noise, demonstrating superior performance to existing variational methods for imputation and de-noising with different real data sets. We further show higher classification accuracy after imputation, proving the advantage of propagating uncertainty to downstream tasks with our model. |
| Researcher Affiliation | Collaboration | Francesco Tonolini School of Computing Science University of Glasgow Glasgow, UK 2402432t@student.gla.ac.uk Pablo Garcia Moreno Amazon London, UK morepabl@amazon.co.uk Andreas Damianou Amazon London, UK damianou@amazon.co.uk Roderick Murray-Smith School of Computing Science University of Glasgow Glasgow, UK roderick.murray-smith@glasgow.ac.uk |
| Pseudocode | Yes | Algorithm 1 Training the TAE Model |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We corrupt the MNIST dataset (Deng, 2012), We further evaluate our TAE with Fashion-MNIST 28 28 grey-scale images of clothing (Xiao et al., 2017), and the UCI HAR dataset, which consists of filtered accelerometer signals from mobile phones worn by different people during common activities (Anguita et al., 2012). |
| Dataset Splits | No | The paper mentions repeating experiments and using subsets of data for classification tasks (e.g., '10,000 examples... 1,000 of these are labelled... classify the remaining 9,000'), but does not provide explicit train/validation/test dataset splits with percentages or sample counts for the main model training. |
| Hardware Specification | Yes | All experiments were performed using a Titan X GPU. |
| Software Dependencies | No | The paper mentions 'Tensorflow' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | Common parameters are as follows: 500, 000 iterations with the ADAM optimiser in Tensorflow, an initial training of 2e-4 and batch size of 20. The hyper-parameters specific to the TAE are instead: γ initially set to 0.01 and then linearly increased to 1 between 50, 000 and 100, 000 iterations, λ = 2 and C = 10. |