Summary of Lora-ensemble: Efficient Uncertainty Modelling For Self-attention Networks, by Michelle Halbheer et al.
LoRA-Ensemble: Efficient Uncertainty Modelling for Self-attention Networks
by Michelle Halbheer, Dominik J. Mühlematter, Alexander Becker, Dominik Narnhofer, Helge Aasen, Konrad Schindler, Mehmet Ozgur Turkoglu
First submitted to arxiv on: 23 May 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 The paper introduces LoRA-Ensemble, a parameter-efficient deep ensemble method for self-attention networks that tackles the challenge of calibrated uncertainty estimates in machine learning. By extending Low-Rank Adaptation (LoRA) to an implicit ensembling approach, the authors propose a single pre-trained self-attention network with shared weights across all members and member-specific low-rank matrices for attention projections. This method outperforms explicit ensembles in terms of calibration and achieves similar or better accuracy on various prediction tasks and datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes machine learning algorithms more accurate by giving them a way to measure how sure they are about their predictions. Right now, many methods make overconfident guesses that aren’t really based on the data. The authors developed a new method called LoRA-Ensemble that helps solve this problem without needing to train lots of separate models. They showed that this approach works well and is more efficient than other methods. |
Keywords
» Artificial intelligence » Attention » Lora » Low rank adaptation » Machine learning » Parameter efficient » Self attention
