Summary of Evolutionary Retrofitting, by Mathurin Videau (tau) et al.
Evolutionary Retrofitting
by Mathurin Videau, Mariia Zameshina, Alessandro Leite, Laurent Najman, Marc Schoenauer, Olivier Teytaud
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE); Optimization and Control (math.OC)
GrooveSquid.com Paper Summaries
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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 presents a new approach called AfterLearnER that refines fully-trained machine learning models by optimizing carefully chosen parameters or hyperparameters using non-differentiable optimization methods. The authors demonstrate the efficiency of this method by applying it to various tasks, including depth sensing, speech re-synthesis, image quality assessment in 3D GANs, and code translation. They also show that AfterLearnER can be used dynamically at inference time to take into account user inputs. The advantages of this approach include its versatility, ability to use non-differentiable feedback, limited overfitting, and anytime behavior. Additionally, AfterLearnER requires only a minimal amount of feedback, which is significantly less than what’s typically required in related works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to improve machine learning models by making small changes to the model itself. The method, called AfterLearnER, can be used for lots of different tasks, like making better images or understanding speech. It’s helpful because it doesn’t need a lot of data and can make decisions on its own without needing human input all the time. This is important because it makes the models more useful in real-life situations. |
Keywords
» Artificial intelligence » Inference » Machine learning » Optimization » Overfitting » Translation
