Weighted Sum Aggregation is a fundamental linear algebraic operation within machine learning that computes a new vector by taking the dot product between fixed weights and input vectors, followed by summation to produce a weighted average representation. Formally defined as $y = \sum_{i} w_i x_i$, this mechanism reduces dimensionality while preserving relative information magnitudes across high-dimensional embedding spaces in continuous probability domains. It serves as the canonical method for feature pooling or query-key matching precursors, establishing the theoretical basis for transforming unstructured inputs into fixed-size context vectors prior to non-linear processing stages.
The Self Attention Mechanism is a neural network architecture mechanism defined by linear projections into query, key, and value subspaces that compute context-dependent representations through scale…