Code and data accompanying our SVRHM'21 paper.

Last update: Nov 17, 2021

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Deep Learning svrhm21_RNN_explain

Overview

Code and data accompanying our SVRHM'21 paper.

Requires tensorflow 1.13, python 3.7, scikit-learn, and pytorch 1.6.0 to be installed.

Python scripts included:

RNN_gen.py can be used to train the RNNs.
RNN_analyse_reprs_recurrence.py can be used to train linear classifiers for auxiliary variables and category decoding, on the layer activations and recurrent flows
RNN_perturb.py can be used to perform the perturbation analyses.

Two pretrained RNNs can be downloaded from https://osf.io/pf4u5/ to be assessed using the jupyter notebook 'SVRHM21_results.ipynb'. Alternatively, you can train your own RNN and assess it.

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Code and data accompanying our SVRHM'21 paper.

Related tags

Overview

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