Summary of Pareto Low-rank Adapters: Efficient Multi-task Learning with Preferences, by Nikolaos Dimitriadis et al.
Pareto Low-Rank Adapters: Efficient Multi-Task Learning with Preferences
by Nikolaos Dimitriadis, Pascal Frossard, Francois Fleuret
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Pareto Front Learning (PFL) is a machine learning approach that enables selecting desired operational points during inference by parameterizing the Pareto Front (PF) with a single model. Unlike traditional Multi-Task Learning (MTL), which optimizes for a single trade-off decided prior to training, PFL allows for flexibility in task weighting. However, recent PFL methodologies suffer from scalability limitations, slow convergence, and excessive memory requirements, while exhibiting inconsistent mappings from preference to objective space. PaLoRA, a novel parameter-efficient method, addresses these issues by augmenting any neural network architecture with task-specific low-rank adapters that continuously parameterize the PF in their convex hull. This approach enables faster convergence and strengthens the validity of the mapping from preference to objective space throughout training. Experiments show that PaLoRA outperforms state-of-the-art MTL and PFL baselines across various datasets, scales to large networks, and reduces memory overhead compared to competing PFL baselines in scene understanding benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about a new way to teach machines to do many things at once. It’s called Pareto Front Learning (PFL). PFL is better than other methods because it lets the machine choose how much to focus on each task during inference. The problem with current PFL methods is that they can be slow and use too much memory. This paper proposes a new method, PaLoRA, which solves these problems by adding special adapters to neural networks. These adapters help the network learn general and specific features for each task. The results show that PaLoRA does better than other methods on various tasks and uses less memory. |
Keywords
» Artificial intelligence » Inference » Machine learning » Multi task » Neural network » Parameter efficient » Scene understanding
