What is a candidate sampling?
A training-time optimization that calculates a probability for all the positive labels, using, for example, softmax, but only for a random sample of negative labels.
candidate sampling explained in plain English
A training-time optimization that calculates a probability for all the positive labels, using, for example, softmax, but only for a random sample of negative labels. For instance, given an example labeled beagle and dog, candidate sampling computes the predicted probabilities and corresponding loss terms for: - beagle - dog - a random subset of the remaining negative classes (for example, cat, lollipop, fence). The idea is that the negative classes can learn from less frequent negative reinforcement as long as positive classes always get proper positive reinforcement, and this is indeed observed empirically. Candidate sampling is more computationally efficient than training algorithms that compute predictions for all negative classes, particularly when the number of negative classes is very large.
Example
Practitioners refer to candidate sampling when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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