Transformers
MATE
Introduced by Eisenschlos et al. in MATE: Multi-view Attention for Table Transformer EfficiencyMATE 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.
Source: MATE: Multi-view Attention for Table Transformer Efficiency
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Automatic Speech Recognition (ASR) | 1 | 7.69% |
| Domain Generalization | 1 | 7.69% |
| Language Modelling | 1 | 7.69% |
| Speech Recognition | 1 | 7.69% |
| Camera Calibration | 1 | 7.69% |
| 3D Object Classification | 1 | 7.69% |
| Point Cloud Classification | 1 | 7.69% |
| Multi-agent Reinforcement Learning | 1 | 7.69% |
| Reinforcement Learning (RL) | 1 | 7.69% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
