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arXiv Machine Learning By Heyang Gong

Toward Machine Learning with the Unit as a Primitive: Learning from Unit-Linked Events

Read the original on arXiv Machine Learning →

The paper proposes treating the ‘unit’—a persistent referent that multiple events may refer to—as an explicit primitive in machine learning tasks. It formalizes supervised learning as learning a pair of a tokenizer that generates a contextual unit token and a shared response law that uses this token, thereby distinguishing homogeneous from heterogeneous worlds. The work also introduces concepts such as unit abduction and trusted resolvers to handle cases where unit identity is unresolved.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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