Optimal Binary Autoencoding with Pairwise Correlations
Authors: Akshay Balsubramani
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments in Section 6 show extremely competitive results with equivalent fully-connected autoencoders trained with backpropagation. The datasets we use are first normalized to [0, 1], and then binarized by sampling each pixel stochastically in proportion to its intensity, following prior work (Salakhutdinov & Murray (2008)). |
| Researcher Affiliation | Academia | Akshay Balsubramani Stanford University abalsubr@stanford.edu |
| Pseudocode | Yes | Algorithm 1 Pairwise Correlation Autoencoder (PC-AE) Input: Size-n dataset ˆX, number of epochs T |
| Open Source Code | Yes | TensorFlow code available at https://github.com/aikanor/pc-autoencoder . |
| Open Datasets | Yes | The datasets we use are first normalized to [0, 1], and then binarized by sampling each pixel stochastically in proportion to its intensity, following prior work (Salakhutdinov & Murray (2008)). We use the preprocessed version of the Omniglot dataset found in Burda et al. (2016), split 1 of the Caltech-101 Silhouettes dataset, the small not MNIST dataset, and the UCI Adult (a1a) dataset. |
| Dataset Splits | No | The paper mentions 'early stopping on the test set' and '10-fold cross-validation' for 'not MNIST', but it does not explicitly describe a separate validation dataset split with specific percentages or counts for all experiments. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments, such as CPU/GPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions 'TensorFlow code available at https://github.com/aikanor/pc-autoencoder', and the use of 'Adagrad (Duchi et al. (2011))' and the 'Adam method with default parameters (Kingma & Ba (2014))' for optimization, but it does not specify version numbers for these software components or libraries. |
| Experiment Setup | Yes | We used minibatches of size 250. All standard autoencoders use the Xavier initialization and trained for 500 epochs or using early stopping on the test set. We compare to a basic AE with a single hidden layer, trained using the Adam method with default parameters (Kingma & Ba (2014)). |