Methods › Sequential › Sequence To Sequence Models › GTS

Goal-Driven Tree-Structured Neural Model

GTS

46 papers tagged archive 2025-07-28

Introduced by Zhipeng Xie and Shichao Sun in A Goal-Driven Tree-Structured Neural Model for Math Word Problems

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

The archive carries no description for this method.

PaperSource

Papers archive 2025-07-28

30 shown of 46, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 60 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Representation Learning10
Graph Representation Learning7
Graph Classification6
Node Classification6
Graph Learning4
Fairness3
Math3
Deep Reinforcement Learning2
Graph Property Prediction2
Graph Regression2
Image Super-Resolution2
Inductive Bias2
Math Word Problem Solving2
Object Detection2
Reinforcement Learning2
Super-Resolution2
object-detection2
reinforcement-learning2
3D Anomaly Detection1
3D Anomaly Detection and Segmentation1

Usage over time archive 2025-07-28

Papers per year tagged with GTS: 2019 to 2025, peak 18 18 0 2019: 3 papers 2019 2020: 2 papers 2020 2021: 3 papers 2021 2022: 6 papers 2022 2023: 7 papers 2023 2024: 18 papers 2024 2025: 7 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (46 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Sequence To Sequence Models

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