How deeply does Sutskever's List cover inductive bias?

Traces architectural biases through memory behavior, task accuracy, language-model results, and parsing generalization.

Inductive bias

An inductive bias is a set of assumptions built into a learning system that favors some functions or explanations over others.

An inductive bias is a set of assumptions built into a learning system that favors some functions or explanations over others. Architecture can shape which relationships a model attends to and how it generalizes. section 7.4.1 section 5.1.1

Relational memory

A Relational Memory Core (RMC) emphasizes connections among memories rather than storing information in isolation. On WikiText-103, even a single memory slot could attend to different positions in recent context; the model also maintained low perplexity with a shorter evaluation context, consistent with a bias toward salient recent relationships. section 7.4.1

Recurrence and self-attention

A Transformer can connect distant words such as “mice” in one self-attention operation, whereas convolutional models need stacked layers to connect distant positions. A four-layer Transformer also achieved an F1 score of 91.3 on Penn Treebank constituency parsing without task-specific architecture changes. These results suggest recurrence is not essential to the task and may constrain parallelism and long-range modeling. section 5.1.1

How Sutskever's List covers it

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Delivering Machine Learning Projects: From design to deploymentInductive bias is the set of assumptions built into an algorithm or model architecture that favors some patterns or hypotheses over others.
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Causal AIAn inductive bias is an assumption implicit in the choice of a machine learning algorithm that constrains how it generalizes from data.

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