Summary of On the Regularization Of Learnable Embeddings For Time Series Forecasting, by Luca Butera et al.
On the Regularization of Learnable Embeddings for Time Series Forecasting
by Luca Butera, Giovanni De Felice, Andrea Cini, Cesare Alippi
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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 The paper investigates methods to regularize the learning of local learnable embeddings for time series forecasting, aiming to improve performance and transferability. It combines deep learning techniques with regularization strategies to prevent co-adaptation of local and global parameters. The study includes various perturbation-based methods that periodically reset the embeddings during training, showing consistent improvements in widely adopted architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how to make time series forecasting models better by combining different techniques. It shows that regularizing the learning of these embeddings can improve performance and help models work well in new situations. The research also looks at how often to reset the embeddings during training, which is important for developing good foundation models. |
Keywords
» Artificial intelligence » Deep learning » Regularization » Time series » Transferability
