Algebraic Positional Encodings Jean-Philippe Bernardy
–Neural Information Processing Systems
We introduce a novel positional encoding strategy for Transformer-style models, addressing the shortcomings of existing, often ad hoc, approaches. Our framework implements a flexible mapping from the algebraic specification of a domain to a positional encoding scheme, where positions are interpreted as orthogonal operators. This design preserves the structural properties of the source domain, thereby ensuring that the end-model upholds them. The framework can accommodate various structures, including sequences, grids and trees, but also their compositions. We conduct a series of experiments demonstrating the practical applicability of our method. Our results suggest performance on par with or surpassing the current state of the art, without hyper-parameter optimizations or "task search" of any kind. Code is available through https://aalto-quml.github.io/ape/.
Neural Information Processing Systems
May-29-2025, 05:38:54 GMT
- Country:
- Europe (0.67)
- North America > United States (0.28)
- Genre:
- Research Report
- Experimental Study (1.00)
- New Finding (1.00)
- Research Report
- Technology: