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Cross-attention mask in Transformers - Data Science Stack Exchange Cross-attention mask: Similarly to the previous two, it should mask input that the model "shouldn't have access to" So for a translation scenario, it would typically have access to the entire input and the output generated so far So, it should be a combination of the causal and padding mask 👏 Well-written question, by the way
Cross-entropy loss explanation - Data Science Stack Exchange The cross entropy formula takes in two distributions, p(x) p (x), the true distribution, and q(x) q (x), the estimated distribution, defined over the discrete variable x x and is given by
xgboost - What is the proper way to use early stopping with cross . . . I am not sure what is the proper way to use early stopping with cross-validation for a gradient boosting algorithm For a simple train valid split, we can use the valid dataset as the evaluation dataset for the early stopping and when refitting we use the best number of iterations
Using Cross Validation technique for a CNN model 1- Why most CNN models don't use Cross Validation? 2- If I use Cross Validation, how can I generate the confusion matrix? Can I split dataset to train test and then do cross validation on train set as train validation (i e , doing cross validation as train validation except for the usual train test) and at last use test set the same way? Or how?
Choosing the number of features via cross-validation What is the correct standard generally accepted approach in such cases? Related: Which is better: Cross validation or a validation set for hyperparameter optimization? Which hyperparameters are returned as best in cross validation? Remark: A relevant factor here might be that I choose the number of features via sequential backward selection