A loss function is a part of a cost function which is a type of an objective function. All that being said, thse terms are far from strict, and depending on context, research group, background, can shift and …

I'm using TensorFlow for training CNN for classification. In machine learning, there are several different definitions for loss function. In general, we may select one specific loss (e.g., binary c...

The other loss function can be derived in this way by imposing that the probability mass function be a Bernoulli probability mass function. This derivation is ubiquitous so it is not repeated here.

Apr 11, 2018 · A loss function is a measurement of model misfit as a function of the model parameters. Loss functions are more general than solely MLE. MLE is a specific type of probability model …

Jun 27, 2017 · In v3 they use 3 boxes across 3 different "scales" You can try getting into the nitty-gritty details of the loss, either by looking at the python/keras implementation v2, v3 (look for the function …

Sep 16, 2022 · Minimizing square loss implies a maximization (not minimization) of correlation between observed and predicted values, but the reverse implication does not apply. However, since …

And the loss function weights the values larger than this number at only a third of the weight given to values less than it. Thus, it's sort of intuitive that the scales are balanced when the τ τ th quantile is …

Sep 5, 2019 · During training I see the following loss: The first 50k steps of the training the loss is quite stable and low, and suddenly it starts to exponentially explode. I wonder how this can happen. Of …

Jan 23, 2023 · A loss function is differentiable if you can compute its derivative with respect to the model parameters. In your example, there is not enough information to say if the loss function is …

Almost any loss function can be used as a metric, which is quite common. The opposite, however, may not work well since commonly used metric functions (such as F1, AUC, IoU and even binary …