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View Code? Open in Web Editor NEWPAC-Bayes with Backprop - Tighter risk certificates for neural networks
PAC-Bayes with Backprop - Tighter risk certificates for neural networks
Hi, I have found a question in regards to code in lines 530 to 533.
I read about the initialization method for parameters stated in your paper, which is different from common methods. I am not sure about the initialization method used in nn.2Dcov and nn.Linear is in line with yours.
Should the nn.2Dcov and nn.Linear be replaced by the customized 2Dcov and Linear like you did in ProbNNet4l?
Hi, Maria, thanks for your excellent work, which really inspired me a lot. Could you please release the document of the required library version? My PyThon version is 3.7, and the Torch version is 1.7.0. When I try to reimplement your code, there is a RuntimeError: one of the variables “bound_l” needed for gradient computation has been modified by an in-place operation: [torch.FloatTensor [10]] is at version 1; expected version 0 instead. Hint: enable anomaly detection to find the operation that failed to compute its gradient, with torch.autograd.set_detect_anomaly(True). Thanks in advance.
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