Summary of Approximate Attention with Mlp: a Pruning Strategy For Attention-based Model in Multivariate Time Series Forecasting, by Suhan Guo et al.
Approximate attention with MLP: a pruning strategy for attention-based model in multivariate time series forecasting
by Suhan Guo, Jiahong Deng, Yi Wei, Hui Dou, Furao Shen, Jian Zhao
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the effectiveness of attention-based architectures in time series forecasting tasks, particularly spatio-temporal and long-term forecasting. The authors propose a new perspective on self-attention networks, demonstrating that the entire attention mechanism can be replaced with an MLP (multi-layer perceptron) formed by feedforward, skip-connection, and layer normalization operations for temporal and spatial modeling. This finding is significant as it shows that the attention-based networks’ performance remains top-tier even after removing the Q, K, V projection, attention score calculation, dot-product, and final projection. The authors evaluate their approach on spatio-temporal and long-term time series forecasting tasks, achieving a reduction in FLOPS (floating-point operations per second) of 62.579% with less than 2.5% loss in performance for spatio-temporal networks, and 42.233% with less than 2% loss in performance for long-term forecasting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how attention-based models work well for predicting future events in time series data. The authors found a way to simplify these complex models by replacing part of them with simpler calculations. They tested this new approach on two types of prediction tasks: one that involves both spatial and temporal patterns, and another that looks ahead over a long period. The results show that the simplified model still performs very well, with only a small decrease in accuracy. This discovery helps us better understand why attention-based models are so good at making predictions. |
Keywords
» Artificial intelligence » Attention » Dot product » Self attention » Time series
