Deconstructing the Ladder Network Architecture
Authors: Mohammad Pezeshki, Linxi Fan, Philemon Brakel, Aaron Courville, Yoshua Bengio
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This paper presents an extensive experimental investigation of variants of the Ladder Network in which we replaced or removed individual components to learn about their relative importance. |
| Researcher Affiliation | Academia | Mohammad Pezeshki MOHAMMAD.PEZESHKI@UMONTREAL.CA Linxi Fan LINXI.FAN@COLUMBIA.EDU Phil emon Brakel PBPOP3@GMAIL.COM Aaron Courville AARON.COURVILE@UMONTREAL.CA Yoshua Bengio YOSHUA.BENGIO@UMONTREAL.CA Universit e de Montr eal, Columbia University, CIFAR |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | The experimental setup includes two semi-supervised classification tasks with 100 and 1000 labeled examples and a fully-supervised classification task with 60000 labeled examples for Permutation-Invariant MNIST handwritten digit classification. |
| Dataset Splits | No | The paper mentions that 'The test set is not used during all the hyperparameter search and tuning' and that 'Hyperparameters... are tuned separately for each variant and each experiment setting', implying the use of a validation set, but it does not specify the exact split percentages or sample counts for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions algorithms and techniques (e.g., 'ADAM optimization algorithm', 'Batch Normalization'), but it does not specify any software dependencies with version numbers (e.g., Python, TensorFlow, PyTorch versions). |
| Experiment Setup | Yes | All variants and the vanilla Ladder Network itself are trained using the ADAM optimization algorithm (Kingma & Ba, 2014) with a learning rate of 0.002 for 100 iterations followed by 50 iterations with a learning rate decaying linearly to 0. Hyperparameters including the standard deviation of the noise injection and the denoising weights at each layer are tuned separately for each variant and each experiment setting (100-, 1000-, and fully-labeled). |