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The identification methods in this package allow adding a regularisation term to the loss for better convergence. It would be nice if we could regularise not only the parameters and obtained system bu…
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I've just seen [this paper](https://arxiv.org/pdf/2401.10190.pdf). The algorithm they describe is interesting too, and we might want to consider supporting it, but there's a more immediate point. In S…
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To keep the agnostic nature we need to start adding regularisation specific functions to aPIPs/ACE separately. These will then return a Γ matrix, with which we solve the regularised LSQ problem here i…
casv2 updated
2 years ago
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Hi Corey,
Thanks for your great blog post and sharing the code.
I was wondering about the weights in the the cost functions: shouldn't the l2_losses in the _generative_loss_ function have opposi…
sitmo updated
7 years ago
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Add regularisation to all weights other than bias.
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Thank you for a great, clean and well structured implementation.
Although it is said in the paper that pointSAGA is extendable to non-smooth functions it is not immediately clear how l1 regularisat…
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Implement regularisation methods such as L2/L1 or Dropout in a class that can be imported and utilised
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Hello,
I want to add l2 regularisation .Can you tell me where can I add this line:
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4, weight_decay=1e-5)
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Implement l1 and l2 regularisation
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Add the intuitive regularisation functionality to IPFitting. Store the QR factorisation and get regularised coefficients using `IPFitting.Lsq.reglsq()`? Parameters such as τ through lsqfit(). Default …
casv2 updated
4 years ago