Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Pre-training of Recurrent Neural Networks via Linear Autoencoders
Authors: Luca Pasa, Alessandro Sperduti
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Using four well known datasets of sequences of polyphonic music, we show that the proposed pre-training approach is highly effective, since it allows to largely improve the state of the art results on all the considered datasets. |
| Researcher Affiliation | Academia | Luca Pasa, Alessandro Sperduti Department of Mathematics University of Padova, Italy EMAIL |
| Pseudocode | Yes | Algorithm 1 shows in pseudo-code the main steps of our procedure. |
| Open Source Code | No | The paper does not state that the code for their proposed linear autoencoder pre-training methodology is open-source or provide a link to it. It only mentions using a third-party Theano-based software for RNN training. |
| Open Datasets | Yes | In order to evaluate our pre-training approach, we decided to use the four polyphonic music sequences datasets used in [21] for assessing the prediction abilities of the RNN-RBM model. |
| Dataset Splits | Yes | Each dataset is split in training set, validation set, and test set. Statistics on the datasets, including largest sequence length, are given in columns 2-4 of Table 1. |
| Hardware Specification | Yes | Time in seconds needed to compute pre-training matrices (Pre-) (on Intel c Xeon c CPU E5-2670 @2.60GHz with 128 GB) and to perform training of a RNN with p = 50 for 5000 epochs (on GPU NVidia K20). |
| Software Dependencies | No | The paper mentions 'Theano-based stochastic gradient descent software' but does not provide a specific version number for Theano or any other software dependency. |
| Experiment Setup | Yes | Our pre-training approach (Pre T-RNN) has been assessed by using a different number of hidden units (i.e., p is set in turn to 50, 100, 150, 200, 250) and 5000 epochs of RNN training |