Recently, researchers have leveraged the Denoising AutoEncoder (DAE) to reduce the noise in side-channel acquisitions (a.k.a. traces) that reduces the effectiveness of key recovery. Taking the L2 Loss ...

Abstract: Recently, researchers have leveraged the Denoising AutoEncoder (DAE) to reduce the noise in side-channel acquisitions (a.k.a. traces) that reduces the effectiveness of key recovery. Taking ...

I am writing a custom loss function to calculate val_loss (mean squared error) while ignoring NANs. My data is time series involving 3 features and 1 target (4 variables in total). My target in ...

pyplot.legend() pyplot.show() Conclusion In this blog, we have covered most of the loss functions that are used in deep learning for regression and classification problem. Further, we can experiment ...

The model is trained until the loss is minimized and the data is reproduced as closely as possible. Through this process, an autoencoder can learn the important features of the data. While that’s a ...

The loss function is a method of evaluating how well the algorithm performs on your dataset, most of the people are confused about the difference between loss function and the cost function. We will ...