aiqbench analysis
RecipeNet introduces a hierarchical Transformer architecture designed specifically to model the sequential and structured nature of procedural data in fields like materials science and pharma, overcoming limitations of traditional tabular methods that flatten such information. By capturing field-level interactions within steps and dependencies across them, this approach offers significant improvements for representation learning tasks involving complex process recipes.
RecipeNet: A Hierarchical Transformer for Recipe Data
arXiv:2608.14505v1 Announce Type: new Abstract: Recipe data arises in domains such as materials synthesis, pharmaceutical formulation, and industrial manufacturing, where procedures are represented as ordered sequences of steps containing heterogeneous structured fields. Existing tabular learning methods typically flatten this structure into fixed-schema representations, limiting their ability to capture hierarchical field interactions and procedural dependencies. We propose RecipeNet, a hierarchical Transformer architecture that encodes field-level interactions within each step and sequential dependencies across steps through stacked Transformer encoders. Experiments on multiple recipe datasets and tasks demonstrate that RecipeNet consistently outperforms existing tabular models, highlighting the value of hierarchical and sequential modeling for recipe representation learning.
Abstract:Recipe data arises in domains such as materials synthesis, pharmaceutical formulation, and industrial manufacturing, where procedures are represented as ordered sequences of steps containing heterogeneous structured fields. Existing tabular learning methods typically flatten this structure into fixed-schema representations, limiting their ability to capture hierarchical field interactions and procedural dependencies. We propose RecipeNet, a hierarchical Transformer architecture that encodes field-level interactions within each step and sequential dependencies across steps through stacked Transformer encoders. Experiments on multiple recipe datasets and tasks demonstrate that RecipeNet consistently outperforms existing tabular models, highlighting the value of hierarchical and sequential modeling for recipe representation learning.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2608.14505 [cs.LG]
(or arXiv:2608.14505v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2608.14505 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1145/3799682.3839874 Focus to learn more DOI(s) linking to related resources
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