Summary of Low-rank Adaptation Of Time Series Foundational Models For Out-of-domain Modality Forecasting, by Divij Gupta et al.
Low-Rank Adaptation of Time Series Foundational Models for Out-of-Domain Modality Forecasting
by Divij Gupta, Anubhav Bhatti, Suraj Parmar, Chen Dan, Yuwei Liu, Bingjie Shen, San Lee
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Signal Processing (eess.SP)
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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 LoRA (Low-Rank Adaptation) is a popular technique for fine-tuning pre-trained models across different modalities and tasks. This paper investigates applying LoRA to foundational time series models, such as Lag-Llama, MOIRAI, and Chronos, and its potential impact on forecasting vital signs of sepsis patients in ICUs. The authors demonstrate LoRA’s ability to adapt these models to unseen out-of-domain modalities, aiming to enhance forecasting performance while reducing inefficiencies associated with fine-tuning large models on limited data. Experiment results show that LoRA fine-tuning significantly improves forecasting accuracy, achieving comparable results to state-of-the-art models trained from scratch. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a technique called LoRA (Low-Rank Adaptation) and how it can be used with special types of AI models called foundational time series models. These models are designed to predict future events based on past data. The researchers want to see if LoRA can help these models make better predictions, especially when dealing with new kinds of data they haven’t seen before. They tested LoRA on some specific tasks and found that it worked well, improving the accuracy of the predictions. |
Keywords
» Artificial intelligence » Fine tuning » Llama » Lora » Low rank adaptation » Time series
