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Code for the paper 'Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity' accepted in ICML 2024

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tools
tools
 
 
README.md
README.md
 
 
__init__.py
__init__.py
 
 
data_seperation.py
data_seperation.py
 
 
dataloader.py
dataloader.py
 
 
eval.py
eval.py
 
 
eval_RSM.py
eval_RSM.py
 
 
eval_RSM_learning_curve.py
eval_RSM_learning_curve.py
 
 
eval_RSM_learning_curve_noPlan.py
eval_RSM_learning_curve_noPlan.py
 
 
eval_decision_prior.py
eval_decision_prior.py
 
 
eval_decoder.py
eval_decoder.py
 
 
eval_decoder_sample.py
eval_decoder_sample.py
 
 
eval_sensorimotor_process.py
eval_sensorimotor_process.py
 
 
eval_tsne.py
eval_tsne.py
 
 
learner.py
learner.py
 
 
matplot_selector.py
matplot_selector.py
 
 
modules.py
modules.py
 
 
plot_learning_curves.py
plot_learning_curves.py
 
 
train.py
train.py
 
 
transformer.py
transformer.py
 
 
utils.py
utils.py
 
 

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Modular End-to-End Network (MoNet)

Code implementation of the paper, 'Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity' accpeted in ICML 2024.

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Code for the paper 'Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity' accepted in ICML 2024

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