Conveyance: A Versatile Framework for Learning in Structured Class Spaces
While machine learning (ML) architectures have evolved rapidly to account for complex data, loss functions like cross-entropy remain mostly structure-agnostic in many real-world applications. However, the "class-symmetric" nature of these standard losses fundamentally limits the ability of ML models to exploit structural relationships between classes, particularly when facing structured noise. We propose Conveyance, a new classification approach and associated loss function tailored to structured class spaces. It allows users to encode graph-like relations between classes without having to define complex joint distributions or manually tune utility matrices. Technically, our loss function...
| Authors | Yasser Taha, Grégoire Montavon, Nils Körber |
|---|---|
| Primary category | cs.LG |
| Published | 27 May 2026 |
| Last updated at source | 29 May 2026 |
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