Summary of Towards Efficient Pareto Set Approximation Via Mixture Of Experts Based Model Fusion, by Anke Tang et al.
Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion
by Anke Tang, Li Shen, Yong Luo, Shiwei Liu, Han Hu, Bo Du
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a novel approach for solving multi-objective optimization problems in large deep neural networks. Existing methods, including evolutionary algorithms, hypernetworks, and hypervolume-maximization techniques, are computationally expensive and have limited scalability. The authors draw inspiration from model merging and introduce a mixture of experts (MoE) based model fusion method that effectively captures trade-offs between multiple objectives and approximates the entire Pareto set. This approach leverages ensembling the weights of specialized single-task models, which can be unloaded once the routers are learned, introducing no additional computational cost during inference. The authors conduct extensive experiments on vision and language tasks using large-scale models like CLIP-ViT and GPT-2, demonstrating efficient approximation of the entire Pareto front. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us solve a big problem in machine learning. When we want to optimize multiple things at once, like making an image look good and also being easy to recognize, it can be very hard because our models are so complex. The authors came up with a new way to do this using something called mixture of experts (MoE) that lets us combine lots of small models into one big model. This makes it much faster and more efficient than other methods that try to optimize multiple things at once. |
Keywords
» Artificial intelligence » Gpt » Inference » Machine learning » Mixture of experts » Optimization » Vit
