Candidate Sampling
Candidate sampling is a family of training-time optimization techniques used in machine learning to reduce the computational cost of models that must choose among a very large number of output classes.
Neural Networks
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Cross-encoder
A cross-encoder is a neural network architecture that scores a pair of texts by feeding them jointly into a single transformer (such as BERT) and reading out one scalar score for the pair, for example a…
Embedding Layer
An embedding layer is a neural network component that acts as a trainable lookup table, mapping discrete integer indices (such as word IDs, user IDs, or category codes) to dense, continuous-valued vectors.
Full Softmax
Full softmax (also called the standard softmax or exact softmax) is the softmax computation that calculates a probability for every possible output class in a classification model