GitHub - mcmahon-lab/The-Benefits-of-Self-Supervised-Learning-for-Training-Physical-Neural-Networks: Repository that contains the code to reproduce the results presented in the paper.

Name		Name	Last commit message	Last commit date
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.DS_Store		.DS_Store
End-to-end SSL - CNN - non-linear projectors - CIFAR.ipynb		End-to-end SSL - CNN - non-linear projectors - CIFAR.ipynb
Layer-wise SSL - CNN - non-linear projectors - CIFAR - Residual connections (standard).ipynb		Layer-wise SSL - CNN - non-linear projectors - CIFAR - Residual connections (standard).ipynb
Layer-wise SSL - CNN - non-linear projectors - CIFAR - asymetric augmentations.ipynb		Layer-wise SSL - CNN - non-linear projectors - CIFAR - asymetric augmentations.ipynb
Layer-wise SSL - CNN - non-linear projectors - CIFAR - simultaneous.ipynb		Layer-wise SSL - CNN - non-linear projectors - CIFAR - simultaneous.ipynb
Layer-wise SSL - CNN - non-linear projectors - CIFAR.ipynb		Layer-wise SSL - CNN - non-linear projectors - CIFAR.ipynb
Layer-wise SSL - CNN - non-linear projectors - MNIST.ipynb		Layer-wise SSL - CNN - non-linear projectors - MNIST.ipynb
Layer-wise SSL - MLP - fixed non-linear projectors - simultaneous perturbations stochastic approximation - Barlow twins-Copy1.ipynb		Layer-wise SSL - MLP - fixed non-linear projectors - simultaneous perturbations stochastic approximation - Barlow twins-Copy1.ipynb
Layer-wise SSL - MLP - fixed non-linear projectors - simultaneous perturbations stochastic approximation - Barlow twins.ipynb		Layer-wise SSL - MLP - fixed non-linear projectors - simultaneous perturbations stochastic approximation - Barlow twins.ipynb
Layer-wise SSL - MLP - fixed non-linear projectors - simultaneous perturbations stochastic approximation - stochastic sequential objective - SGD.ipynb		Layer-wise SSL - MLP - fixed non-linear projectors - simultaneous perturbations stochastic approximation - stochastic sequential objective - SGD.ipynb
Layer-wise SSL - MLP - fixed non-linear projectors - simultaneous perturbations stochastic approximation - stochastic sequential objective.ipynb		Layer-wise SSL - MLP - fixed non-linear projectors - simultaneous perturbations stochastic approximation - stochastic sequential objective.ipynb
Layer-wise SSL - MLP - fixed non-linear projectors - simultaneous perturbations stochastic approximation.ipynb		Layer-wise SSL - MLP - fixed non-linear projectors - simultaneous perturbations stochastic approximation.ipynb
Layer-wise SSL - MLP - non-linear projectors.ipynb		Layer-wise SSL - MLP - non-linear projectors.ipynb
SSL - MLP - MNIST - End-to-end.ipynb		SSL - MLP - MNIST - End-to-end.ipynb
Supervised learning - MLP - MNIST -End-to-end.ipynb		Supervised learning - MLP - MNIST -End-to-end.ipynb
plot-notebook.ipynb		plot-notebook.ipynb
utils_conv.py		utils_conv.py
utils_conv_asym.py		utils_conv_asym.py
utils_conv_end_to_end.py		utils_conv_end_to_end.py
utils_conv_simul.py		utils_conv_simul.py
utils_mlp.py		utils_mlp.py
utils_mlp_endtoend.py		utils_mlp_endtoend.py
utils_mlp_spsa.py		utils_mlp_spsa.py
utils_mlp_spsa_stochastic_objective.py		utils_mlp_spsa_stochastic_objective.py
utils_mlp_spsa_weighted_sum.py		utils_mlp_spsa_weighted_sum.py

mcmahon-lab/The-Benefits-of-Self-Supervised-Learning-for-Training-Physical-Neural-Networks

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