Methods › General › Deep Tabular Learning › MATE

MATE

14 papers tagged archive 2025-07-28

Introduced by Julian Martin Eisenschlos et al. in MATE: Multi-view Attention for Table Transformer Efficiency

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

MATE is a Transformer architecture designed to model the structure of web tables. It uses sparse attention in a way that allows heads to efficiently attend to either rows or columns in a table. Each attention head reorders the tokens by either column or row index and then applies a windowed attention mechanism. Unlike traditional self-attention, Mate scales linearly in the sequence length.

PaperSource

Papers archive 2025-07-28

14 shown of 14, 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 39 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
Language Modelling2
Point Cloud Classification2
Question Answering2
3D Object Classification1
3D Point Cloud Classification1
Aspect-Based Sentiment Analysis1
Attribute1
Automatic Speech Recognition1
Automatic Speech Recognition (ASR)1
Camera Calibration1
Code Generation1
Computational Efficiency1
Contrastive Learning1
Cross-Modal Retrieval1
Decoder1
Descriptive1
Domain Generalization1
Entity Linking1
Fake News Detection1
GPU1

Usage over time archive 2025-07-28

Papers per year tagged with MATE: 2021 to 2025, peak 5 5 0 2021: 2 papers 2021 2022: 2 papers 2022 2023: 2 papers 2023 2024: 5 papers 2024 2025: 3 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (14 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

Deep Tabular LearningTable Question Answering ModelsTransformers

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