Summary of Rough Transformers For Continuous and Efficient Time-series Modelling, by Fernando Moreno-pino et al.
Rough Transformers for Continuous and Efficient Time-Series Modelling
by Fernando Moreno-Pino, Álvaro Arroyo, Harrison Waldon, Xiaowen Dong, Álvaro Cartea
First submitted to arxiv on: 15 Mar 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces the Rough Transformer, a novel neural network architecture designed to efficiently model irregularly sampled time-series data with long-range dependencies. The approach combines Neural ODE-based models and Transformer architectures to capture both local and global dependencies in input data. Unlike traditional recurrent models, the Rough Transformer operates on continuous-time representations of input sequences, significantly reducing computational costs while maintaining performance. The paper also proposes multi-view signature attention, which utilizes path signatures to augment vanilla attention and capture dependencies. Experimental results show that Rough Transformers outperform their counterparts on synthetic and real-world time-series tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Rough Transformer is a new way to analyze medical data that’s not always collected at the same time intervals. This makes it hard for computers to understand the patterns in the data. The researchers created a new model that uses two different techniques: Neural ODE-based models and Transformer architectures. These help capture both short-term and long-term connections in the data. They also developed a way to make this model more efficient, so it can handle longer sequences of data without using up too much computer power. This is important because medical data often involves lots of variables and complex patterns. The results show that this new model outperforms others on real-world data. |
Keywords
* Artificial intelligence * Attention * Neural network * Time series * Transformer
