RecipeNet: A Hierarchical Transformer for Recipe Data
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 Trans
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- LinkedLinked via arxiv author · 85%Pin-Yen Huang →
“RecipeNet: A Hierarchical Transformer for Recipe Data”
- LinkedLinked via arxiv author · 85%Sachin Chhabra →
“RecipeNet: A Hierarchical Transformer for Recipe Data”
- LinkedLinked via arxiv author · 85%Prasanth Sai Gouripeddi →
“RecipeNet: A Hierarchical Transformer for Recipe Data”
- LinkedLinked via arxiv author · 85%Abhinav Kumar Singh →
“RecipeNet: A Hierarchical Transformer for Recipe Data”
- LinkedLinked via arxiv author · 85%Baoxin Li →
“RecipeNet: A Hierarchical Transformer for Recipe Data”