arXiv:2606. 04045v1 Announce Type: cross Abstract: Representation learning is often described as preserving the information in an input that is relevant for prediction.
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arXiv:2407. 12288v5 Announce Type: replace-cross Abstract: The progress of machine learning over the past decade is undeniable.
arXiv:2606. 01092v1 Announce Type: cross Abstract: Supervised learning evaluates predictors through their input-output behavior.
arXiv:2606. 19366v1 Announce Type: cross Abstract: Information lattice learning (ILL) learns interpretable rules of a signal by alternately projecting the signal onto a partition lattice that encodes a hierarchy of abstractions and lifting selected rules back to the signal domain.
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.
arXiv:2609.39525v1 Announce Type: new Abstract: Casting Bayesian inference as a neural network optimization problem targeting an amortized posterior is attractive, as it extends to otherwise intracta...
arXiv:2608. 13510v1 Announce Type: cross Abstract: Machine learning procedures are commonly evaluated in terms of predictive accuracy and computational efficiency.