Gradient descent viewed as attention over examples
Published on February 6, 2023, the article interprets gradient-trained linear layers as memories of training examples and describes predictions as unnormalized attention over training experience.
Source: Gradiente descendente como atenção sobre exemplos de treino (arxiv.org). Text prepared with AI from this source.
What happened and what to do
Published on February 6, 2023, the article represents gradient-trained linear layers as key-value memories containing training examples and initial weights. In this interpretation, predictions use unnormalized dot-product attention over training experience, connecting training dynamics with the attention mechanism.
A company developing or evaluating models could apply this perspective in interpretability experiments: organize training data and model outputs in an analysis pipeline, then investigate how changes to examples relate to predictions. A dashboard can help technical teams examine these relationships and inform data and model evaluations.
How the consultancy can help
Wendelmaques can diagnose the opportunity, define an analysis scope, and implement and operate pipelines and dashboards to relate training data, model behavior, and internal evaluations.
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